The Fusemachines AI Fellowship isn’t just about getting into AI; it’s about personal growth. Through practical projects, intensive training, and mentorship from industry leaders, fellows gain the skills to thrive in AI’s evolving landscape.

In a candid conversation, Pawan shares his insights, reflecting on his journey through the Fusemachines AI Fellowship program. Join us as we dive into Pawan’s experiences, gaining valuable insights and inspiration from his remarkable story of resilience and determination.

Can you tell us a bit about yourself and your background before joining the AI Fellowship program?

– Of course! Before I embarked on my journey with the AI Fellowship program at Fusemachines, my background was rooted in a deep passion for technology and innovation. Growing up, I was always drawn to the world of computers and fascinated by their potential to transform our lives. Pursuing my education in engineering was a natural progression for me, as it allowed me to delve deeper into this exciting field.

During my academic journey, I became increasingly intrigued by the burgeoning field of Artificial Intelligence. The prospect of creating intelligent systems capable of mimicking human cognitive functions ignited a fire within me. I was determined to explore this frontier and contribute to the advancement of AI technology.

Before joining the AI Fellowship program, I had already begun to dabble in AI through self-study and online courses. However, I knew that to truly master this complex discipline, I needed a structured learning environment and hands-on experience. That’s when I discovered the Fusemachines AI Fellowship program – a unique opportunity to immerse myself in AI and gain invaluable insights from industry experts.

What motivated you to apply for the AI Fellowship program initially?

– My motivation to apply for the AI Fellowship program was fueled by several factors. Firstly, I had developed a strong interest in Artificial Intelligence through self-study and online courses. However, I recognized the limitations of theoretical learning and sought an opportunity to apply my knowledge in real-world settings.

The AI Fellowship program offered the perfect blend of structured learning and hands-on experience, providing me with the opportunity to work on meaningful projects under the guidance of industry experts. I was particularly drawn to the program’s emphasis on practical skills development, which I believed would be instrumental in advancing my career in AI.

Additionally, I was inspired by the success stories of past fellows who had transitioned into successful AI professionals after completing the program. Their journeys served as a testament to the program’s effectiveness in preparing individuals for the challenges of the AI industry. I was eager to follow in their footsteps and leverage the resources and support offered by the program to achieve my own career goals in AI.

In summary, my motivation to apply for the AI Fellowship program stemmed from a desire for practical experience, a recognition of the program’s effectiveness in preparing individuals for AI careers, and the opportunity to learn from and be inspired by successful AI professionals who had gone before me.

Could you share with us the challenges you faced during your first attempt at the program, and how did you overcome them?

– During my first attempt at the AI Fellowship program, I encountered several challenges that tested my resolve. One of the main obstacles was underestimating the level of preparation required for the program. Despite my passion for AI and my theoretical understanding of the concepts, I realized that I had not fully grasped the depth and breadth of the material.

Moreover, I found it challenging to balance my academic commitments with the intensive preparation required for the program. Time management became a significant hurdle as I struggled to allocate sufficient time and energy to both my coursework and AI studies.

To overcome these challenges, I adopted a more structured approach to my preparation, focusing on strengthening my weaknesses and refining my time management skills. I sought guidance from mentors and peers who had successfully navigated the program, leveraging their insights to tailor my study plan and prioritize my efforts effectively.

What were the key learnings from your first attempt, and how did you prepare differently for your successful second attempt?

– The key learnings from my first attempt at the AI Fellowship program were instrumental in shaping my approach for the successful second attempt. One of the most important realizations was the need for comprehensive preparation that went beyond theoretical understanding. I recognized that practical application and hands-on experience were essential for mastering the concepts and skills required for success in AI.

To prepare differently for my second attempt, I adopted a more structured and focused approach to my studies. I prioritized practical projects and real-world applications, seeking opportunities to apply my knowledge in meaningful ways. Additionally, I dedicated more time to honing my technical skills and addressing areas of weakness identified during my first attempt.

Moreover, I sought guidance from mentors and peers who had successfully completed the program, leveraging their insights and advice to refine my study plan and strategy. Their mentorship provided invaluable support and guidance, helping me navigate the challenges more effectively.

I embraced a growth mindset, viewing setbacks as opportunities for learning and growth rather than failures. I remained resilient in the face of adversity, staying committed to my goals and continuously striving for improvement.  By prioritizing practical experience, seeking mentorship, and maintaining a growth mindset, I was able to overcome the challenges and achieve success in the AI Fellowship program.

Can you walk us through the moment you found out you were accepted into the program for the second time? What emotions were you experiencing?

– The moment I found out I was accepted into the program for the second time was truly surreal. I vividly remember checking my email, heart pounding with anticipation, and seeing the subject line that read, “Congratulations! You’ve been accepted into the AI Fellowship program.”

A wave of emotions washed over me – relief, joy, and an overwhelming sense of accomplishment. It was a validation of the hard work, dedication, and perseverance that had gone into my preparation for the program. All the late nights spent studying, the moments of doubt and uncertainty, and the setbacks along the way suddenly felt worth it.

I couldn’t help but feel a profound sense of gratitude – to the mentors and peers who had supported me throughout my journey, to the Fusemachines team for believing in my potential, and to myself for never giving up despite the challenges I faced. It was a moment of immense pride and satisfaction, knowing that my determination had paid off and that I was one step closer to realizing my dreams in AI.

In that moment, I felt unstoppable – ready to take on whatever challenges lay ahead and to make the most of this incredible opportunity. It was a turning point in my journey, reaffirming my belief in the power of perseverance and the limitless possibilities that await those who dare to chase their dreams.

Now that you’re working as an ML Engineer Associate at Fusemachines, how has the fellowship program prepared you for your current role?

– The fellowship program has played a pivotal role in preparing me for my current role as an ML Engineer Associate at Fusemachines. Firstly, the program provided me with a solid foundation in AI concepts and methodologies, equipping me with the technical skills needed to excel in the field. From machine learning algorithms to deep learning frameworks, the program covered a wide range of topics essential for my role as an ML engineer.

The hands-on projects and real-world applications offered during the program allowed me to put my theoretical knowledge into practice. Working on challenging projects alongside industry experts provided invaluable experience and insights into the day-to-day responsibilities of an ML engineer. I gained practical experience in data preprocessing, model training, evaluation, and deployment, which has proven invaluable in my current role.

Lastly, what advice would you give to aspiring students  determined to pursue a career in AI applying for this edition of the fellowship program?

– ! If you’re eager to dive into the world of AI and considering applying for the fellowship program, I’ve got some tips to help you out. First off, make sure you’re comfortable with the basics – things like linear algebra, probability, and statistics. Brush up on those concepts, and don’t forget about basic calculus too.

Since Python is a big part of the program, it’s essential to be fluent in it. Spend some time coding and working on Python projects to get comfortable with the language. Also, remember to prepare for the entrance exam. You’ll need to show off your programming skills in Python, as well as your understanding of math concepts like linear algebra and calculus. Plus, there’ll be some IQ and problem-solving tests thrown in there too.

But remember, your learning doesn’t stop with the program itself. It’s crucial to seek out opportunities to apply your skills in real-world projects and gain practical experience. Look for internships, research opportunities, or personal projects where you can put your AI knowledge into practice and learn from hands-on experience.

Additionally, don’t underestimate the value of mentorship. Finding a mentor who can provide guidance, support, and insights can be incredibly valuable in your journey. Reach out to professionals in the field, network with fellow AI enthusiasts, and don’t hesitate to ask for advice or mentorship opportunities.

With these tips in mind, you’ll be well-prepared to tackle the fellowship program and embark on a fulfilling journey in AI. Good luck!

Bottom Line  

As aspiring fellows embark on their own AI journeys, it’s essential to remember that success is not just about academic knowledge but also about practical experience, mentorship, and a willingness to embrace challenges. By staying committed to their goals, seeking out opportunities for growth, and surrounding themselves with a supportive network of mentors and peers, aspiring AI professionals can overcome obstacles and achieve remarkable success. 

The Fusemachines AI Fellowship program offers a unique opportunity for individuals to realize their full potential in AI, providing a platform for learning, growth, and professional development. With the right mindset and preparation, aspiring fellows can leverage this program to kickstart their careers in AI and make meaningful contributions to the field.

The post From Setback to Success: The Inspiring Journey of Fusemachines AI Fellow appeared first on Fuse AI.

What is the Fusemachines AI Fellowship Program?

The Fusemachines AI Fellowship Program is a training and education program lasting 6 months designed to create advanced career opportunities in AI. With a 100% scholarship, this program is led by leading faculty members from renowned US universities and industry experts. Graduates of the program gain a competitive edge in the job market and establish a strong foundation for their AI careers.

The fellowship program consists of a non-credited industrial training program that spans 6 months. Participants engage in two-hour online learning sessions twice a week. Additionally, the students are required to spend 10 hours per week on practical assignments and other tasks. Upon completion, participants receive well-regarded certifications that hold great value in the tech industry.

Interested in joining the AI fellowship program Latin America 2023? Apply Now

To learn more about Fusemachines and its education programs, click here.

Why You Should Consider Applying to the Fellowship Program

If you are passionate about AI and data science, the Fusemachines AI Fellowship Program provides a unique opportunity to kickstart your career. Here are some compelling reasons to consider joining the program:

Expertly Designed Program: The program is meticulously crafted by a dedicated academic team of instructors and engineers, ensuring participants receive the best education and training in AI.

Real-World Assignments and Projects: As a fellow, you will gain valuable AI exposure through hands-on assignments and projects, allowing you to apply your knowledge to real-world scenarios.

Self-Paced Immersive Learning: The program offers independent self-paced immersive learning, allowing participants to tailor their learning experience based on their individual needs.

Networking Opportunities: The fellowship program provides a platform for networking with peers, AI professionals, and the alumni community. These connections can lead to valuable collaborations and open doors to exciting career opportunities.

Placement Opportunities: Graduates of the program have the advantage of accessing placement opportunities at Fusemachines. This industry collaboration enhances the prospects of securing fulfilling roles in the field of AI.

Full Scholarship: The Fusemachines AI Fellowship Program offers a full scholarship with no hidden or extra costs. It is a golden opportunity to receive high-quality education and training without financial burden.

Eligibility Criteria for Joining the Program

To be eligible for the AI Fellowship Program, you must meet the following criteria:

Educational Background: You can join the program if you are a 4th-year engineering or IT student, a graduate, or a professional with a solid understanding of Linear Algebra, Probability, Statistics, and Basic Calculus.

Programming Skills: Proficiency in Python programming and basic knowledge of Computer Science is essential.

Soft Skills: fluency in English, good communication, teamwork, and a learning attitude are highly valued in candidates applying for the program. 

By meeting these criteria, you are ready to take the first steps toward a successful career in AI.

Interested in joining the AI fellowship program Latin America 2023? Apply here. 

To learn more about Fusemachines and its education programs, click here. 

AI Fellowship Latin America 2023 Program Outcomes

The Fusemachines AI Fellowship Program equips participants with a range of skills and knowledge. Upon completing the program, fellows can expect the following outcomes:

Data Science and Machine Learning Skills: Fellows will gain expertise in data science and machine learning, acquiring hands-on experience in problem-solving and project implementation. They will learn to select and implement appropriate algorithms, libraries, frameworks, and techniques to tackle various AI challenges.

Understanding of AI Algorithms: Participants will develop a solid understanding of Artificial Intelligence, Machine Learning, and Deep Learning algorithms. They will grasp the underlying mathematics and programming concepts and be able to run experiments to improve algorithms through code modifications.

End-to-End Pipeline Design: The program equips fellows with the ability to assess performance, evaluate and compare different models, and design and deploy end-to-end AI pipelines. This holistic knowledge allows for comprehensive AI solutions.

Program outcome highlights 

• Machine Learning Foundation: Regressions, Classification, Clustering, Performance Metrics, Reinforcement Learning
• Neural Network and Deep Learning: Artificial Neural Networks, Tensorflow / Pytorch, Training, Evaluating and Fine tuning Models, CNNs, RNNs, GANs, and SOTA Models, Deep Reinforcement Learning
• Computer Visions, Object Detection, Image Segmentation, Image Generation, 
• Natural Language Processing: Text Extraction, NER, Sentiment Analysis
• Generative AI: Large Language Models LLMs, Image Generations, Prompt engineering
• MLOPs: Deployment, Monitoring, ML as a Service

AI Fellowship Journey: From Application to Deadline

The journey to becoming an AI fellow begins with the application process. Here is a step-by-step overview of what to expect:

Step 1 Online Application

Start by completing the online application form and uploading your CV. You will receive a confirmation email shortly after submitting the application.

Step 2 Online Proctored Entrance Test

Qualified applicants will be required to take an hour-long aptitude test. The test will assess various areas, including programming (Python), math (Linear Algebra, Calculus, Probability, and Statistics), IQ, problem-solving, and behavioral questions.

Step 3 Interview of Shortlisted Candidates

Shortlisted candidates will undergo an interview process. This step aims to evaluate their compatibility with the program and select the most suitable individuals for the fellowship.

Step 4 Enrollment and Onboarding

Congratulations to those who successfully complete the interview process! The final step is to complete the enrollment and onboarding procedures. Once done, you will officially join the fellowship program and embark on an exciting AI learning journey.

FAQs

Here are some frequently asked questions about the Fusemachines AI Fellowship Program:

Is prior experience in AI required to apply for the fellowship program?

No prior experience in AI is required. The program is designed to accommodate participants from diverse backgrounds, including those who are new to AI. However, prior experience will definitely help you on the proctored entrance tests. 

How long is the duration of the program?

The program duration is 6 months, with multiple assessments and practical assignments. 

Are there any financial obligations or costs associated with the program?

No. Once selected, the program is 100% free, including training, education, assessments, and certifications. 

Are there any job placement or networking opportunities after completing the program?

Yes. Upon completion of the program, there are job placement and networking opportunities available to enhance your AI journey.

Is the program only for students, or can professionals apply too?

Yes. Professionals, as well as 4th-year engineering and IT students and graduates, are eligible to apply for the program.

Are there any post-program support or alumni benefits?

Fusemachines values its fellowship alumni and provides ongoing support and benefits. Alumni may have access to exclusive events, networking opportunities, job placement assistance, and continued learning resources to further enhance their AI careers.

Bottom Line

Embarking on a career in AI can be daunting without proper guidance and support. The Fusemachines AI Fellowship Latin America Program 2023 provides a platform dedicated to creating and nurturing AI talent. 

The Fusemachines AI Fellowship Latin America Program 2023 is a game-changing opportunity for individuals passionate about AI. With its comprehensive curriculum, hands-on projects, and networking opportunities, this program sets the stage for a successful AI career. Apply today and embark on a transformative journey that will shape the future of AI in Latin America.

Remember, the deadline for applications is fast approaching. Don’t miss out on this remarkable opportunity. Apply now and unleash your potential in the exciting world of AI!

Interested in joining the AI fellowship program Latin America 2023? Apply here.

To learn more about Fusemachines and its education programs, click here.

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What is the Fusemachines AI Fellowship Program?

Fusemachines AI Fellowship is a year-long program to train, educate, and create advanced AI career opportunities with a 100% scholarship. The program is created by leading US university faculty members and AI industry experts. Graduates increase their potential for competitive job opportunities and a solid platform to elevate their professional journey in AI. 

Program Structure

The fellowship is a non-credited industrial training program lasting 1 year. You’ll have two-hour online learning sessions twice a week and occasional onsite sessions. The MicrodegreeTM in AI program is the primary compulsory program. You can then pursue the MicrodegreeTM Specialization program. Both these programs consist of 2 courses each lasting 3 months. Graduates are provided with certifications that are well-regarded in the tech industry.

Why you should consider applying to the fellowship program

This program is one of its kind in Nepal providing opportunities for students and professionals to start their careers in artificial intelligence and data science. The demand for talent is high and you can find rewarding roles by pursuing a career in it. Here are some of the many reasons to join the fellowship. 

• The program is designed by a dedicated academic team of instructors and engineers
• Fellows get AI exposure through real-world assignments and projects 
• Students benefit from independent self-paced immersive learning
• Valuable networking opportunities with peers, AI professionals, and alumni community
• Graduates get placement opportunities at Fusemachines 
• All students get a full scholarship with no hidden/extra costs

Eligibility Criteria for joining the program

You are eligible to join the program if you are a 4th year engineering and IT student, a graduate, or a professional who has sound knowledge of

• Linear Algebra, Probability, Statistics, and Basic Calculus
• Python programming and basics of Computer Science

That’s it! The course is designed such that you will be taught the basics of AI and machine learning. Apply today if you are ready to take the first steps to a bright and rewarding future.

AI Fellowship 2023 Program Outcomes 

Still wondering what you can achieve with the program? Here are some outcomes to help your decision: 

• Fellows will garner data science and ML skills with hands-on experience in problem-solving and projects. They’ll also be able to select and implement appropriate algorithms, libraries, frameworks, and techniques for different problems.
• Fellows will develop a solid understanding of Artificial Intelligence, Machine Learning, and Deep Learning algorithms with an understanding of underlying math and programming. They’ll also be able to run experiments to change details in code to improve algorithms.
• Fellows can assess the performance, and evaluate and compare different models to design and deploy an end-to-end pipeline.

Here is what the stepwise selection process looks like:

Step 1: Online application

Fill out the form and upload your CV. You’ll then get a confirmation email within a few hours of the application. Click here to get started: 

Step 2: Online Proctored Entrance Test

You will be required to take an hour-long aptitude test in multiple choice questions (MCQ) format. Some topics assessed include:

• Programming; Python programming
• Math; Linear Algebra, Calculus, Probability, and Statistics
• IQ and Problem-solving
• Behavioral Questions

Step 3: Interview of Shortlisted Candidates

This is the final step and selected candidates from here will be a part of the Fusemachines AI Fellowship program 2023.

Step 4: Enrollment and Onboarding

Congratulations on being selected. After completing the enrollment and onboarding steps, you are ready to learn alongside other fellows and be the talent that drives the industry forward. 

Bottom line

Pursuing a career in AI can be challenging without proper guidance. Kickstart your AI career with a platform dedicated to create and nurture AI talent. The application deadline for this iteration of the program is December 15th, 2022. Don’t miss this opportunity. Apply today!

The post Fusemachines AI Fellowship 2023: All you need to know  appeared first on Fuse AI.

What is Artificial Intelligence (AI) Marketing, and How does it Work?

Artificial Intelligence (AI) marketing makes automated decisions based on collected data and conducts further assessments of audience or consumption patterns that may influence marketing efforts. Teams can widely use AI for smart marketing in initiatives where speed is everything.

Likewise, AI systems understand how to communicate with customers based on data and customer profiles. Furthermore, these systems also deliver personalized content at the right time without human intervention, assuring maximum productivity.

Similarly, many marketers employ AI for smart marketing to assist their teams, or carry out more tactical tasks that require less human expertise. Such tasks can include automated decision-making, data analysis, and real-time personalization, among others.  

AI in Marketing: What It Is and What It Isn’t

Artificial Intelligence is essential in helping marketers connect with customers. As such, the following AI marketing components make up today’s leading solutions. They can help bridge the gap between customer data acquired and the practical steps marketing teams can take for future campaigns-

• Solutions for AI Platforms

AI-powered solutions give you a single platform to manage massive amounts of data. These platforms then provide you with actionable marketing knowledge about your target audience. Consequently, the platform then allows you to make data-driven judgments about how to best communicate with customers. Frameworks like Bayesian Learning and Forgetting, for example, help marketers determine how responsive a customer is to a particular marketing tactic.

• Machine Learning (ML)

Artificial Intelligence drives machine learning– algorithms that study data and improve automatically over time. The machine learning devices, thus, assess new data in the context of historical data. Subsequently, the assessment allows them to make decisions based on what has or has not worked successfully.

• Metrics and Big Data

Similarly, with the rise of digital media and big data, marketers can also evaluate their efforts better. Teams can also correctly allocate value across channels. To elaborate, there is an overabundance of data. Hence, many marketers face challenges when it comes to establishing which data sets are worth gathering.

How Can Artificial Intelligence (AI) Be Used in Marketing?

A well-thought-out strategy is critical when using AI in marketing campaigns and operations. It will help marketing teams avoid costly difficulties. Additionally, a good strategy can also help teams quickly get the most out of their AI investment.

Before using AI technology for marketing efforts, there are a few critical factors to consider:

• Objectives

Firstly, as with any marketing program, it is vital to establish defined goals and marketing metrics from the beginning. Start by identifying regions inside campaigns or procedures where AI could assist, or segmenting. To enhance customer experience, defining KPIs will reveal how successful the AI-enhanced campaign has been.

• Data Protection Regulations

Secondly, when you begin your AI program, make certain that your AI platform will not cross the border of acceptable data-use in the name of personalization. Thus, to preserve compliance and consumer trust, make sure you concretely define privacy rules. Then, program them into platforms as needed.

• Sources and Quantity of Data

Similarly, to get started with AI marketing, marketers have to have access to a multitude of data on client preferences, external trends, and other variables that will affect ad performance. As such, teams can glean this information through the company’s CRM, marketing initiatives, and website. Likewise, marketers can also use second-and third-party data. This can include location, weather, and other environmental factors that influence a purchase.

• Recruit Data Science Experts

The next point to consider is this- many marketing teams lack data science and artificial intelligence skills. This can make it challenging to work with large amounts of data and derive insights. Thus, to get projects off the ground, companies should collaborate with third-party organizations that help with data collecting, analysis, and continue maintenance.

• Ensure Data Quality

Finally, machine learning systems learn how to make decisions by ingesting more data. However, insights are ineffective when data is not standardized or error-free. AI algorithms may even make decisions that harm campaigns. Hence, before using AI marketing, marketing teams must work with data management teams and other business lines to build data cleansing and management processes.

Choosing a Machine Learning Platform

A vital initial step when it comes to AI for smart marketing is selecting the right platform for launching an AI marketing campaign. Marketers should be careful to identify gaps the platform attempts to address and choose solutions based on their capabilities. For example, products used to boost customer satisfaction with AI require different functionality than tools used to improve speed and productivity. 

Additionally, keep in mind the level of transparency you’ll need to understand why an AI platform came to a particular conclusion. Depending on the algorithm, marketing teams may receive a clear report on why AI made particular decisions, and which data influenced the conclusion.

You can also read our previous blog about how businesses can leverage AI: 10 Benefits and Applications of AI in Business.

Those who do not use AI marketing will be replaced by those who do. According to Gartner, folks in charge of marketing insights will no longer be as successful in this new marketing landscape if AI is not leveraged.

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After all, every company must progress in this industry because every company offers a product that provides support to users. Consumers use technology and link to network providers in order to “stay connected” and wield the power of looking up almost any information in the palm of their hands. Such product lines include Siri, Google Allo, and Bixby, among many others. There are also a plethora of goods from different companies on the market that use AI methods to help users obtain relevant data or complete required tasks.

Samsung and AI

Similarly, Samsung also uses AI to provide personalized content suggestions to users. The Samsung Research Center has seven AI centers in five countries: Korea (Seoul), Canada (Montreal and Toronto), Moscow, the United Kingdom (Cambridge), and the United States (New York and Silicon Valley), all working towards providing its clients with fresh AI-based insight and value. Several of the brand’s most recent products have also used Machine Learning (ML) to allow users to enjoy and explore cutting-edge future generation AI techs.

In like manner, along with Samsung, Apple and Huawei have also imbued their smartphones with AI chips capable of doing nearly 5 trillion operations per second while consuming significantly less power.

At a forum session in 2020, Samsung presented its long-term humanist vision for its AI strategy. The company aspires to provide purpose-built applications that improve end-user experiences. Rather than chasing new-fangled technology, Samsung is more interested in emphasizing personalization, seeking to assist its users in discovering items that meet their needs and budgets while also providing exclusive experiences.

Listed below are some ways AI in Samsung Products is used.

Samsung’s Virtual Assistant Bixby

Like all virtual assistants, Bixby’s goal is to make customers’ lives easier and more convenient. It does so by assisting them in completing activities and answering inquiries by understanding language and speech patterns based on their requests.

Bixby’s features include:

• Bixby Routines: Bixby observes exclusive behavior patterns and suggests an automated task routine.
• Making Enhanced Predictions: As per the received input, Bixby guesses the user’s requirements and offers personalized recommendations.
• Managing the User’s House: Bixby can switch TV channels, switch house lights on and off, and also control the thermostat.
• Quick Command: Once the user sets a trigger command, Bixby can execute a set of tasks in a particular order. These commands can include generating a command for a “morning routine,” which can incorporate sharing the weather report or playing a morning playlist.

Household Robots

Samsung Bot Handy was unveiled as an AI-powered domestic robot during the Consumer Electronics Expo in January 2021.

Although the robot is still in development, it has been designed to perform chores and lend a helping hand around the house. The robot may help the user by doing laundry, filling the dishwasher, setting the table, arranging groceries and laundry, and even pouring a glass of wine.

Smart TVs

AI enables Samsung TVs to provide viewers with personalized content recommendations. Meanwhile, NLP allows users to control volume, apps, and channels with voice requests.

Likewise, Machine Learning is also constantly improving the resolution quality of the platform’s Smart TVs. AI upscales the resolution to 8k to provide viewers with an engaging picture and an excellent viewing experience.

Smartphone Cameras

Samsung’s AI-powered smartphone cameras have improved visual detection capabilities, allowing users to shoot high-quality photos while having fun.

Samsung cameras also shoot better portrait photos. It does so because of its capacity to recognize faces. The AI can also optimize exposure and skin tones in changing lighting conditions. Additionally, the AI also aids in storage by allowing saved pictures to be categorized, allowing users to locate the images they are seeking as they browse quickly.

You can also read our previous blog that details how Google uses AI: How Google uses AI to Improve Search.

Conclusion

AI and Machine Learning can revolutionize technology. AI in Samsung products and other leading manufacturers continuously integrate ML and AI algorithms in fresh and appealing ways. Doing so offers enhanced experiences for their customers. It also helps maintain its place amidst the competition.

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 Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision courses. Certifications like these will help engineers become leading AI industry experts. It also aids them in achieving a fulfilling and ever-growing career in the field.

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In line with the company’s mission to democratize AI, the program, launched in 2017, aims to bridge the gap between the demand and supply of AI talent. Featuring courses created by US university professors and leading AI experts, the AI Fellowship Program is one of the best ways to build a strong AI foundation and efficiently enter the AI workforce. 

To learn more about the program, we spoke to Isu Shrestha, a Level III Machine Learning Engineer at Fusemachines who completed the Fellowship Program in December of 2019. Here’s the conversation.

Tell us about your AI journey. How did you become interested in AI?

I spent years working as a musician and a part-time web developer. I was learning about life and myself, and at one point, I decided that I was ready to take the next step. Consequently, I started sketching out 3 fundamental goals that my new direction in life should fulfill. It should be interesting to me (because I want to enjoy my work), I want to be able to provide for my family (financially sustainable), and I want it to have the potential to impact the world in a meaningful way. This is how I came to AI, the intersection of these 3 things.

How did you choose the AI Fellowship Program?

I was part of an earlier program at Fusemachines where 10,000 people were trained on an online course. Luckily, I got selected for this scholarship, and my understanding of the company’s goal, the democratization of AI, became clear and cohesive. I joined the AI Fellowship program to be a part of this mission.

Tell us about your career/academic footing after the program.

There is a lot of math involved in AI, and it is not easy to learn these topics on your own. It is also hard to know what is essential to understand and what is not. Joining the program gave me access to insights from veterans in the field. It gave me a sneak peek at what was to come when I joined the industry. To put it in a nutshell, I got a job at the top AI company in the country. I think that speaks for itself.

How would you describe the program? 

The program was fun, but it was also intense in a significant way. I felt like it was challenging, but in a way that made me want to work harder. There were many concepts that were difficult to understand, but together we learned and understood how they worked. There was a sense of camaraderie and a sense of privilege to be with some of the smartest minds in the country. My favorite course was Machine Learning because that was the one that needed the most discussion and team input. 

How did the AI Fellowship Program prepare you for your career?

It gave me a glimpse of what was to come. It showed me the building blocks of more sophisticated algorithms. I learned how the fundamentals of Machine Learning were created, and so it made my career a lot easier. I have been able to tackle industry problems with consistency.

What do programs like Fusemachines’ AI Fellowship offer to countries like Nepal?

The contribution is immense. This is the answer to Nepal’s brain drain. Most people leave Nepal because there is a lack of opportunity. But programs like the AI Fellowship allow people to enter the field. It is an opportunity and an initial step. What comes after depends on a drive for success. 

What advice would you give to someone who wants to build a career in AI?

When you are a baby and try to learn to speak, you copy someone else’s words and learn how to speak. It is the same thing when you grow up and try to play an instrument. You learn how to play someone else’s song before learning how to make one yourself. I would advise someone to copy an ML algorithm. Try to replicate it yourself using code. See how it works. Play with it. This is the way to be prepared for the industry. Understand the basics, and the rest will come to you as though you’re playing with Legos.

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These systems are integrated into many of Google’s other products, such as Assistant, Maps, and the recently announced Android Earthquake Alert System. 

Before RankBrain, AI engineers hand-coded 100% of Google’s algorithm. Although humans still work on the algorithm, RankBrain tweaks it on its own in the backend, increasing or decreasing the search query content based on the keyword, backlinks, content length, content freshness, and domain authority. 

RankBrain also looks at how users interact with new search results. If users like the new algorithm better, RankBrain makes it permanent. If not, the AI rolls back the old algorithm. In other words, RankBrain’s function can be divided into two: understanding search queries (keywords) and measuring user satisfaction. 

How RankBrain Functions

So, how does RankBrain understand search queries (keywords)? It does so by matching keywords that Google has not seen before to keywords that Google has seen before. Before RankBrain, Google faced never-before-seen keywords. About 15% were brand new. Since Google processed billions of searches per day, this amounted to around 450 million brand new keywords. Google scanned pages searching for the exact searched keyword. But because the keywords were brand new, Google could not precisely decipher what searchers wanted, and so it guessed.

Let’s say a user searched for “Artificial Intelligence Curriculum for Beginners.” Google would crawl pages containing terms “Artificial,” “Intelligence,” “Curriculum,” and “Beginner.” Today, RankBrain is capable of understanding what users are asking and provides a 100% accurate set of results by trying to figure out what users mean, like how a human would. 

Google also uses Machine Learning technology called “Word2vec” to understand user intent by turning keywords into concepts. Google’s RankBrain AI goes further than simple keyword-matching; it changes the search term into concepts and tries to locate particular pages that cover that concept. 

RankBrain and User Satisfaction

RankBrain also measures user satisfaction via observation. It shows users a set of search results that it “thinks” they’ll like, and if lots of users like one particular page in the results, RankBrain will give that page a rankings boost. 

Similarly, if users don’t interact with certain results or if results have higher bounce rates, RankBrain will drop that page and replace it with another and measure the performance. This is how Google uses AI to analyze UX signals to measure user satisfaction, including organic click-through rates, dwell time, and bounce rate. 

Artificial Intelligence is an extremely important part of modern society, enabling human capabilities to be undertaken by increasingly effective, efficient, and low-cost software. The automation of abilities by AI, like RankBrain for example, creates new opportunities in consumer applications and even in business sectors. 

Read our previous blog about the top AI Trends of 2022: Top 22 AI Trends of 2022.

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 Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision courses. Certifications like these will help engineers become leading AI industry experts. It also aids them in achieving a fulfilling and ever-growing career in the field.

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The field revolutionized computation and other branches of science, including AI. Scientists predict that quantum computing will deliver solutions for machine learning and AI problems due to its proficiency in holding many possible outcomes in the “quantum state,”- a fluid condition that allows the system to be in more than one state simultaneously.  

At present, CPUs, and even GPUs, are limited to classical binary computers, but engineers are always looking for ways to breach its limitations. Deep Learning, a subset of ML, is already pushing the functional boundaries of traditional computers. AI engineers are also adopting novel microprocessor architectures that perform better than conventional CPUs. Similarly, large transformer models capable of functioning on billions of parameters, such as OpenAI’s GPT-3, are also already in use. 

The next step now is Quantum computers, which can solve a broad range of advanced AI problems.   

A Brief Introduction to Quantum Computing 

Quantum Computing aims to develop computers armed with quantum mechanics. The principles of quantum theory are based on energy and material behavior on an atomic and subatomic level. The focus of quantum computing is mobilizing quantum states to perform calculations. Because Quantum computers use quantum physics to perform intricate computations, they can outperform even the best binary supercomputers. 

Classical computers (our smartphones and laptops) encode figures in binary bits (0s or 1s). The basic memory unit in a Quantum computer, however, is a quantum bit, called the qubit. Physical systems, such as electrons spinning or photon orientation, make qubits. Similar to Schördinger’s cat, these biological systems exist in many arrangements at once. This property is known as “the Quantum Superposition.” 

Qubits can be linked together through quantum entanglement—the result is a series of qubits representing different things simultaneously. For example, a classical computer can represent any number between 0 and 255 using eight bits. A quantum computer can define any number between 0 and 255 using eight qubits at the same time. A few hundred coiled qubits can represent more numbers than there are atoms in the universe!

Real-World Examples of Quantum Computers 

One of the largest tech companies in the world- Google, has plans to build its quantum computer by 2029. The company has established a campus in California called Google AI to achieve this goal. Once instigated, Google could introduce quantum computing services via the cloud, enabling companies to access quantum technology without building one themselves. 

Similarly, many other companies, such as Honeywell International (HON) and International Business Machine (IBM), also plan on building and implementing quantum computers. JPMorgan Chase and Visa are also looking into this technology. In fact, IBM is expected to hit a significant quantum computing milestone in the coming years, with plans already underway to have a 1,000-qubit quantum computer by 2023. 

However, commercial use of quantum computers is still unavailable. Currently, research organizations, laboratories, and universities that are part of IBM’s Quantum Network can access IBM’s machines. Companies can also access Microsoft’s quantum technology via the Azure Quantum platform.

Quantum AI 

Quantum AI can be defined as running Machine Learning algorithms using quantum computing. Both quantum computing and AI are dynamic technologies, and AI does need quantum computing to achieve further progress as the computational capabilities of traditional computers limit it. AI can tackle even more complex problems and perhaps even evolve to Artificial General Intelligence (AGI) through quantum computing. Suffice to say, quantum AI can help achieve results that are not possible to achieve with classical computers.

Once again, Google is one of the early quantum computer manufacturers with plans to improve and innovate. Google launched TensorFlow Quantum (TFQ) in March 2020 in collaboration with the University of Waterloo, X, and Volkswagen. This new open-source library combines the TensorFlow Machine Learning development library with the world of quantum computers. Developers can model and create Quantum Neural Network projects capable of running on quantum computers with TFQ.

Why is Quantum AI Important?

Quantum computing will eliminate the obstacles between ANI (Artificial Narrow Intelligence) and AGI (Artificial General Intelligence). Scientists and engineers can use quantum computing to train machine learning models and create optimized algorithms. 

Backed by quantum computing, AI could potentially accomplish years of analysis in a shorter time. Some current AI challenges include Neuromorphic Cognitive Models, adaptive ML, and reasoning under uncertainty. 

Current Application of Quantum Computing and AI 

Despite being unavailable for commercial use, applications of currently available quantum computers are already changing the AI landscape. Below are some examples:     

Processing Large Datasets

Every day, we produce about 2.5 exabytes of data, with 3.2 billion global internet users feeding data banks through social media platforms, in addition to data we create when we take pictures and videos and open accounts, save documents, and so on.

Quantum computers can manage such vast amounts of data even more effectively than classical computers. They can also uncover patterns and spot anomalies. Developers can now better manage quantum bits with each iteration of quantum computer design.

Solving Complex Problems

Calculations that could take years to solve with classic computers, quantum computers complete in seconds. This capability is known as Quantum Supremacy. Quantum computing allows developers to do multiple measures with multiple inputs simultaneously. It is also critical for processing the large amount of data businesses generate daily. Such quick calculation can help solve complex problems. 

Business Insights and Models

Quantum computing can help produce insights by calculating and analyzing the increasing amount of data industries such as pharmaceutical, finance, and life science generate. Models with quantum technology will lead to better treatments for diseases, decrease financial implosions and improve logistic chains. 

Integrating Multiple Datasets

Quantum computing manages and integrates multiple datasets from multiple sources, making analysis quicker and easier. Quantum computing’s ability to handle large data volumes makes it the best choice for solving business problems.

There’s a chance AI plateaus without enough computing power, and quantum computing could help it advance. The quantum computing market is set to reach $2.2 Billion in a matter of years.

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 Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision courses. 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.

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Data refers to machine-readable information in computing and business. Due to large amounts of user-generated data, also known as “big data,” traditional data management technology is incapable of storing and managing it. As such, big data is complex and comes in different forms, such as structured, unstructured, and semi-structured. 

Because regular data warehouses aren’t capable of processing and analyzing big data, platforms such as Spark, Hadoop, NoSQL databases, have emerged to help enable businesses to collect and set up data ponds as repositories. However, simply collecting and managing data directories isn’t enough to gain business value, and conventional data analytics don’t tap into all the benefits of big data.     

This is where Machine Learning (ML) comes in. Able to spot patterns and manage large amounts of data, Machine Learning takes data analytics to the next level, allowing organizations to extract more value from their data. 

As you plan your career, it is important to understand both the differences between big data and machine learning and where they converge. 

Defining Big Data 

Big Data is information or statistics acquired by large ventures and organizations. What qualifies as “big data” however, varies depending on the skills and tools of those analyzing it. Additionally, due to its magnitude, it is difficult to compute big data manually, and data analysts and scientists tend to categorize it into “columns” based on type and source.

Similarly, data analysts use big data to extract information systematically, identify trends, patterns, and human behavior to make decisions. In order to make good decisions, one has to not only make the best guess about what is going on, but also the best estimate of what will happen in the future. We do this all the time when we predict what other people will do in certain situations, often by identifying repeated behavior patterns.

Likewise, data with many columns offer greater statistical power but is prone to false discovery rates. To boot, expanding capabilities also make big data a moving target. In other words, raw data is constantly being produced, expanding the volume of big data, thus making it harder to make concrete predictions as a result.  

Furthermore, the availability of user-generated data has also grown exponentially with the use of smartphones, Internet of Things (IoT) devices, software logs, cameras, microphones, radio-frequency identification (RFID), and wireless sensor networks. International Data Group Inc. (IDC) predicted that the global data volume would grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020. IDC also predicts that by 2025, there will be 163 zettabytes of data.  

Defining Machine Learning 

A subset of Artificial Intelligence, ML extracts knowledge from data and improves and learns from experience without intervention. In other words, through algorithms and training, ML models process data and deliver predictions. Additionally, many applications also use ML, from medicine and e-mail filtering to speech recognition and Computer Vision (CV).

Now, Artificial Intelligence and Machine Learning are often used interchangeably but are not the same. To read more about the subsets of Machine Learning and how it differs from Artificial Intelligence, read our blog: AI vs. ML – Difference Between Artificial Intelligence and Machine Learning.  

How are Machine Learning and Big Data Related? 

Machine Learning vs Big Data aren’t competing concepts. What’s more, they are not mutually exclusive either. In fact, their combination provides impressive results. On one hand, data analysts feed ML algorithms big data, and the algorithm analyses its potential value. On the other, ML tools use such data-driven algorithms and statistical models to put together data sets. The ML model then draws inferences from identified patterns and makes predictions based on these patterns.  

Comprising ample amounts of raw data, big data correspondingly gives ML systems plenty of materials to derive insights from. In like manner, effective big data management also consequently improves Machine Learning as large quantities of high-quality, relevant data make ML models successful. At the same time, data scientists who create these ML models simultaneously provide a way to manage big data.  

A good example is Netflix’s ML algorithms that understand individual viewing preferences to provide recommendations. Similarly, Google also uses Machine Learning to provide personalized experiences, not only for its search function but also for predictive text in Gmail. Google Maps too, uses ML to give users the best directions. 

How are Machine Learning and Big Data Different? 

The primary focus of data science is data visualization and better presentation. Machine Learning, on the other hand, focuses on learning algorithms and from real-time experience. Thus, for data science, data is the main focus, and for Machine Learning, learning is the main focus. And this is where the difference lies. Given below are key differences between ML and Big Data: 

Which should you choose?

When it comes to Machine Learning vs Big Data, both go hand-in-hand. Hence, familiarity with both is ideal. Comparatively, both fields offer competitive job opportunities and are in high demand. Moreover, professionals in both fields also enjoy similar remuneration packages. Thus, if you have skills in both areas, you will be an essential asset.  

To summarize, choosing Machine Learning vs Big Data depends on your interests. Basically, user-generated data is growing at a fast pace and will continue to grow as time goes on. As a result, the need for data scientists, ML engineers, and other data management and analytics professionals will also increase as more companies opt into big data, Machine Learning, and data visualization tools. Conversely, companies that don’t combine big data and Machine Learning will be left behind. 

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 Choosing Between Machine Learning and Big Data appeared first on Fuse AI.

Concerns over Data Privacy and Security 

A frequently cited issue is privacy and data protection. There are several risks related to AI-based Machine Learning. ML needs large data sets for training purposes, while access to those data sets raises questions. 

An additional problem arises with regard to AI and pattern detection. This AI feature may pose privacy risks even if the AI has no direct access to personal data. An example of this is demonstrated in a study by Jernigan and Mistree where AI can identify sexual orientation from Facebook friendships. The notion that individuals may unintentionally ‘leak’ clues to their sexuality in digital traces is a cause for worry, especially to those who may not want this information out there. Likewise, Machine Learning capabilities also enable potential re-identification of anonymized personal data. 

While most jurisdictions have established data protection laws, evolving AI still has the potential to create unforeseen data protection risks creating new ethical concerns. The biggest risk lies with how some organizations collect and process vast amounts of user data in their AI-based system without customer knowledge or consent, resulting in social consequences. 

Treating Data as a Commodity

Much of the current discourse around information privacy and AI does not take into account the growing power asymmetry between institutions that collect data and the individuals generating it. Data is a commodity, and for the most part, people who generate data don’t fully understand how to deal with this. 

AI systems that understand and learn how to manipulate people’s preferences exacerbate the situation. Every time we use the internet to search, browse websites, or use mobile apps, we give away data either explicitly or unknowingly. Most of the time, we allow companies to collect and process data legally when we agree to terms and conditions. These companies are able to collect user data and sell it to third parties. There have been many instances where third-party companies have scrapped sensitive user data via data breaches, such as the 2017 Equifax case where a data breach made sensitive data, which included credit card numbers and social security numbers of approximately 147 million users, public and open for exploitation.  

Ethical Concerns with AI over Bias and Discrimination 

Technology is not neutral—it is as good or bad as the people who develop it. Much of human bias can be transferred to machines. One of the key challenges is that ML systems can, intentionally or inadvertently, result in the reproduction of existing biases. 

Examples of AI bias and discrimination are the 2014 case where a team of software engineers at Amazon building a program to review resumes realized that the system discriminated against women for technical roles. 

Empirical evidence exists when it comes to AI bias in regards to demographic differentials. Research conducted by the National Institute of Standards and Technology (NIST) evaluated facial-recognition algorithms from around 100 developers from 189 organizations, including Microsoft, Toshiba, and Intel, and found that contemporary face recognition algorithms exhibit demographic differentials of various magnitudes, with more false positives than false negatives. Another example is the 2019 case of legislation vote in San Francisco where the use of facial recognition was voted against, as they believed AI-enabled facial recognition software was prone to errors when used on people with dark skin or women. 

Discrimination is illegal in many jurisdictions. Developers and engineers should design AI systems and monitor algorithms to operate on an inclusive design that emphasizes inclusion and consideration of diverse groups.

Ethical Concerns with AI over Unemployment and Wealth Inequality  

The fear that AI will impact employment is not new. According to the most recent McKinsey Global Institute report, by 2030 about 800 million people will lose their jobs to AI-driven robots. However, many AI experts argue that jobs may not disappear but change, and AI will also create new jobs. Moreover, they also argue that if robots take the jobs, then those jobs are too menial for humans anyway. 

Another issue is wealth inequality. Most modern economic systems compensate workers to create a product or offer a service. The company pays wages, taxes, and other expenses, and injects the left-over profits back into the company for production, training, and/or creating more business to further increase profits. The economy continues to grow in this environment. When we introduce AI into the picture, it disrupts the current economic flow. Employers do not need to compensate robots, nor pay taxes. They can contribute at a 100% level with a low ongoing cost. CEOs and stakeholders can keep the company profits generated by the AI workforce, which then leads to greater wealth inequality. 

Concern over Concentration of Economic Power 

The economic impacts of AI are not limited to employment. A concern is that of the concentration of economic (and political) power. Most, if not all, current AI systems rely on large computing resources and massive amounts of data. The organizations that own or have access to such resources will gain more benefits than those that do not. Big tech companies hold the international concentration of such economic power. Zuboff’s concept of “surveillance capitalism” captures the fundamental shifts in the economy facilitated by AI.  

The development of such AI-enabled concentrated power raises the question of fairness when large companies exploit user data collected from individuals without compensation. Not to mention, companies utilize user insights to structure individual action, reducing the average person’s ability to make autonomous choices. Such economic issues thus directly relate to broader questions of fairness and justice.  

Ethical Concerns with AI in Legal Settings 

Another debated ethical issue is legal. The use of AI can broaden the biases for predictive policing or criminal probation services. According to a report by SSRN, law enforcement agencies increasingly use predictive policing systems to predict criminal activity and allocate police resources. The creators build these systems on data produced during documented periods of biased, flawed, and sometimes unlawful practices and policies.  

At the same time, the entire process is interconnected. The policing practices and policies shape the data creation methodology, raising the risk of creating skewed, inaccurate, or systematically biased data. If predictive policing systems are ingested with such data, they cannot break away from the legacies of unlawful or biased policing practices that they are built on. Moreover, claims by predictive policing vendors do not provide sufficient assurance that their systems adequately mitigate the data either.  

Concerns with the Digital Divide 

AI can exacerbate another well-established ethical concern, namely the digital divide. Divides between countries, gender, age, and rural and urban settings, among others, are already well-established. AI can further exacerbate this. AI is also likely to have impacts on access to other services, thereby potentially further excluding segments of the population. Lack of access to the underlying technology can lead to missed opportunities.

In conclusion, the ethical issues that come with AI are complex. The key is to keep these issues in mind when developing and implementing AI systems. Only then can we analyze the broader societal issues at play. There are many different angles and frameworks while debating whether AI is good or bad. No one theory is the best either. Nevertheless, as a society, we need to keep learning and stay well-informed in order to make good future decisions.  

Furthermore, you can also check our previous blog for more information on AI Ethics: Ethics of Artificial Intelligence.

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. They also aid in achieving a fulfilling and ever-growing career in the field.

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