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.

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.

The post How Google Uses AI to Improve Search 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.

The post 6 Major Ethical Concerns with AI appeared first on Fuse AI.

How it Works 

Machines solve complex problems by learning from large amounts of data, even when the dataset is diverse, unstructured, and inter-connected. Deep Learning, likewise, is similar to how humans learn: from experience. Every time the algorithm performs a task, the machine learns and tweaks itself to improve outcomes. The more Deep Learning algorithms learn, the better they perform.

Deep Learning can also automate predictive analytics. While traditional Machine Learning algorithms are linear, Deep Learning algorithms are assembled in a complex and abstract hierarchy. It drives much of the AI applications and services that improve automation.

Likewise, to achieve an acceptable level of accuracy, Deep Learning programs require massive amounts of training data and processing power. Huge amounts of training data and the power to process all that data were not easily available to programmers until the era of big data and cloud computing. 

Similarly, Deep Learning programming works by directly creating complex statistical models from its own iterative output. Because of this, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. 

To boot, Deep Learning is behind most of the products and services we presently use every day, such as digital assistants, voice-enabled remotes, and credit-card fraud detection, among others. Emerging technologies, such as self-driving cars, also use Deep Learning. 

Furthermore, as the Internet of Things (IoT) continues to become more pervasive, most of the data humans and machines create are unstructured and not labeled, but Deep Learning is able to create precise predictive models despite that.  

Deep Learning Methods

Deep Learning models can be created using various methods. These techniques include Learning Rate Decay, Transfer Learning, Training from Scratch, and Dropout.

Learning Rate Decay

The Learning Rate Decay method, also called Learning Rate Annealing or Adaptive Learning Rate, is the process of adjusting the learning rate to increase model performance and reduce training time. The “learning rate” is the factor that sets the conditions for the model’s operation prior to the learning process. Thus, how much change a model experiences are controlled by this parameter. 

As such, Learning Rates that are too high may result in unstable training processes. On the other hand, Learning Rates that are too small may result in a lengthy training process that may get stuck. The most optimal adaptation of Learning Rate during model training is using techniques that reduce the learning rate over time.  

Transfer Learning 

The Transfer Learning process involves improving a previously trained model. This process requires altering and modifying the internal interfaces of a preexisting network. Engineers and programmers first feed the existing network new data that contains previously unknown classifications. 

Once the programmers adjust the network, the model can perform new tasks with more specific categorizing abilities. An advantage of this method is that it requires much less data, and the computation time is reduced to hours, or even minutes.

Training from Scratch

Training from Scratch requires a developer to collect large labeled data sets and configure a network architecture from the ground up. This technique is useful for new applications, as well as for applications with large numbers of output categories. However, this approach is less common as it requires an excessive amount of data. 

Dropout

The Dropout method helps solve the problem of overfitting in large networks that operate with many parameters. It does so by randomly dropping unit connections from the neural network during training. 

The Dropout method can improve the performance of neural networks on Supervised Learning tasks in areas such as classification of documents, speech recognition, and computational biology.

Examples

Since Deep Learning models process information similar to the human brain, they are applied to many tasks done by humans. The most common examples of programs and software using Deep Learning include image recognition tools, language translations, Natural Language Processing (NLP), medical diagnosis, speech recognition software, stock market trading signals, and network security. These tools have a wide array of applications as well, such as self-driving cars and language translation services.

As a matter of fact, Deep Learning applications are so well-integrated into our daily lives through the products and services we use every day that we don’t even take notice or think about the backend where the complex data processing occurs. 

Some popular Deep Learning examples include:

Financial Services

Many financial institutions use Deep Learning’s predictive analytics to conduct algorithmic stock trading, assess loan approval risks for businesses, detect fraud, and help manage client credit and investment portfolios.

Law Enforcement

Deep Learning algorithms can analyze transactional data and recognize dangerous patterns indicative of fraudulent or criminal activities.

In like manner, law enforcement personnel can also use DL applications, such as speech recognition, and computer vision, to improve the efficiency of an investigative analysis. Such DL models can extract patterns and evidence from images, sound and video recordings, and documents, helping law enforcement analyze large amounts of data quickly and accurately.

Customer Service

The most common example of Deep Learning in customer service is the AI Chatbot, used in a variety of applications and customer service portals. While traditional chatbots use natural language and some visual recognition, like those commonly found in call centers, more sophisticated chatbots are able to determine multiple responses to ambiguous questions through learning. Some virtual assistants include Siri, Alexa, and Google Assistant. 

Healthcare

The digitization of hospital records and images has immensely benefitted the healthcare industry. Image recognition applications that run on Deep Learning support medical imaging specialists and radiologists, helping them analyze images in less time. Likewise, they also aid in medical research. Cancer researchers can use Deep Learning models to automatically detect cancer cells, for example. 

Text Generation 

Deep Learning software, such as Grammarly, is programmed to recognize the grammar and style of text to then be used to automatically create completely new text that matches the proper spelling, grammar, and style of the original text.   

Aerospace and Military 

Deep Learning models, such as Custom CNN, are used to detect objects from satellites. These models identify areas of interest, as well as safe or unsafe zones.

Industrial Automation 

Machine models operating on Deep Learning algorithms help improve worker safety in environments like factories and warehouses by automatically detecting when a worker or object is getting too close to a machine.

Adding Color 

Black-and-white photos and videos can now have color added to them through Deep Learning models. In the past, this was an extremely time-consuming manual process.

Computer Vision 

Deep Learning has enhanced computer vision immensely, helping computers achieve extreme accuracy for image classification, object detection, and segmentation. 

Deep Learning vs Machine Learning

While Deep Learning is a subset of Machine Learning, it differentiates itself from ML through the way it solves problems, by the type of data that it works with, and the methods in which it learns.

Read more about how AI, leveraging ML and DL algorithms, helps modern businesses in this blog: 10 Benefits and Applications of AI in Business. 

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

The post What is Deep Learning? appeared first on Fuse AI.

“AI is a concept bigger than ML, used to create intelligent machines capable of simulating human thinking capability and behavior. Machine Learning, on the other hand, is an application or subset of AI that enables machines to learn from data without being explicitly programmed. In other words, AI is the all-encompassing concept that initially erupted, followed by ML that thrived after.” 

AI vs ML – Major Differences (and Overview)

Artificial Intelligence

Artificial Intelligence studies methods to build intelligent programs and machines to creatively solve problems. It has always been considered the human prerogative.

As such, AI aims to build computer systems that mimic human intelligence. The term “Artificial Intelligence”, thus, refers to the ability of a computer or a machine to imitate intelligent behavior and perform human-like tasks.

Likewise, these tasks include actions such as thinking, reasoning, learning from experience, and most importantly, making decisions. AI systems do not require pre-programming. Rather, they use algorithms.

There are many well-known examples of AI, such as Siri, Google’s AlphaGo, and AI in Chess playing, among many others. 

AI Classification

 AI is classified into three types based on capabilities: Weak AI, General AI, and Strong AI.   

Artificial Narrow Intelligence (ANI) or Weak AI 

Weak, or Narrow AI, performs particular tasks but is incapable of passing as a human outside its defined capacities. Most AI in use today is categorized as Weak AI. It is widely used in science, business, and healthcare.

One of the earliest examples of Weak AI is Deep Blue, the first computer to defeat a human in chess. (Not just any human either: Deep Blue defeated Garry Kasparov in 1996). Another good example of Weak AI is bots. 

Bots are software capable of running simple, repetitive, and automated tasks, such as providing answers to questions such as, “How is the weather?” or “What are some good burger restaurants near me?” Bots pull data from larger systems, such as weather sites or restaurant recommendation engines, and deliver the answer. 

Artificial General Intelligence (AGI) 

Artificial General Intelligence systems perform tasks that humans can with higher efficacy, but only for a particular/single assigned function. They are incapable of doing tasks not assigned to them.

Thus, AGI systems can make decisions and learn without human input. Engineers program AGI machines to produce emotional verbal reactions in response to various stimuli. Examples include chatbots and virtual assistants capable of maintaining a conversation. 

Artificial Super Intelligence (ASI) or Strong AI

Strong Artificial Intelligence is the theoretical next step after General AI, perhaps more intelligent than humans. Right now, AI can perform tasks, but they are not capable of interacting with people emotionally.

Additionally, if you want to know more about AI and its subsets, you can check this blog: What is AI? Artificial Intelligence and its Subsets.

Machine Learning (ML)

Machine Learning is a subset of Artificial Intelligence that deals with extracting knowledge from data to provide systems the ability to automatically learn and improve from experience without being programmed. In other words, ML is the study of algorithms and computer models machines use to perform given tasks. 

There are different types of algorithms in ML, such as neural networks, that help solve problems. These algorithms are capable of training models, evaluating performance and accuracy, and making predictions. Furthermore, ML algorithms use structured and semi-structured data. They also learn on their own using historical data. 

The “learning” in ML refers to a machine’s ability to learn based on data. You can say that ML is a method of creating AI. Additionally, ML systems also recognize patterns and make profitable predictions.

Many fields use Machine Learning, such as the Online Recommender System, the Google Search Algorithm, Email Spam Filters, and Facebook Auto Friend Tagging Suggestion.

Components of Machine Learning 

Datasets: ML Engineers train systems on special collections of samples called datasets. The samples can include texts, images, numbers, or any other kind of data. Usually, it takes a lot of time and effort to create a good dataset.

Features: Features are important pieces of data that function as key components to the solution of tasks. Features demonstrate to the machine what to pay attention to.

Algorithm: Machine Learning algorithms are programs, like math or logic. An algorithm can adjust itself to better performance when exposed to more data. It is a procedure that runs on data to create a machine learning model. Essentially, they perform pattern recognition. Similarly, the accuracy or speed of getting results differs from one ML model to the next depending on the algorithm. 

When it comes to performing specific tasks, software that uses ML is more independent than ones that follow manually encoded instructions. An ML-powered system can be better at tasks than humans when fed a high-quality dataset and the right features. 

Types of Machine Learning 

Supervised Learning

In Supervised Learning, an ML Engineer supervises the program throughout the training process using a labeled training dataset. This type of learning is commonly used for regression and classification. 

Examples of Supervised ML include Decision Trees, Logistic Regression, Naive Bayes, Support Vector Machines, K-Nearest Neighbours, Linear, and Polynomial Regression. 

Hence, Supervised ML is commonly used for language detection, spam filtering, computer vision, search, and classification. 

Semi-Supervised Learning

Semi-Supervised Learning uses a mixture of labeled and unlabeled samples of input data. In this process, the programmers include the desired prediction outcome. The ML model must then find patterns to structure the data and make predictions.

Unsupervised Learning

In Unsupervised Learning, engineers and programmers don’t provide features. Rather, the model searches for patterns independently. Therefore, this type of ML is good for insightful data analytics. The program can recognize patterns humans would miss because of our inability to process large amounts of numerical data. 

That being so, UL can be used to analyze customer preferences based on search history, find fraudulent transactions, and forecast sales and discounts. Examples include K-Means Clustering, Mean-Shift, Singular Value Decomposition (SVD), DBSCAN, Principal Component Analysis (PCA), Latent Dirichlet Allocation (LDA), Latent Semantic Analysis, and FP-Growth. 

Accordingly, engineers commonly use them for data segmentation, anomaly detection, recommendation systems, risk management systems, and fake images analysis.

Reinforcement Learning

Finally, Reinforcement Learning is an ML training method formulated on rewarding desired behaviors, and/or punishing undesired ones. This is very similar to how humans learn: through trial. A reinforcement learning model is capable of perceiving and interpreting its environment, taking actions, and learning through trial and error. 

Furthermore, RL allows engineers and programmers to step away from training on static datasets. Instead, the computer is capable of learning in dynamic environments, such as in video games and the real world. Reinforcement learning works well in in-game research as they provide data-rich environments. 

Some examples include Q-Learning, SARSA, Genetic algorithm, DQN, and A3C. As such, engineers and programmers commonly use them for self-driving cars, games, robots, and resource management.

AI vs ML – Key Differences

We use AI to resolve tasks that require human intelligence. ML, on the other hand, is a subset of AI that solves specific tasks by learning from data and making predictions. For this reason, you can say that all Machine Learning is AI, but not all AI is Machine Learning.

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

The post AI vs ML – Difference Between Artificial Intelligence and Machine Learning appeared first on Fuse AI.

From developing and executing marketing strategies to improving sales function, AI enhances various business functionalities, and helps save time and money while increasing operational efficiency. 

Impacts of AI in Business 

1. Real-time Assistance and Customer Service 

Many businesses, such as airlines, need to be able to communicate with their customers in a timely fashion. AI can assist real-time customer support and communication, from sending personalized travel information to notifying passengers of delays. A positive experience during this time of communication is crucial. Many companies use AI to identify customer needs and deliver the best customer service accordingly. This is done while tackling associated problems and competing with other companies. Scaling to meet the needs of every customer is impossible without AI.     

2. Profit Gain and Efficiency

The pace with which AI handles tasks on a massive scale is unmatched. While AI handles tedious work, humans can focus on more creative and collaborative tasks. This enables businesses to minimize costs related to performing mundane, repetitive tasks and maximize the talent of their human workers. AI can also result in shorter cycles of development. In other words, AI can help businesses move faster and cut down the time between product designing and commercialization. The shortened timeline can also aid in better Returns on Investment (ROIs).    

3. Data Mining

The next benefit of AI in business is data mining. Cloud-based AI offers the advantage of quickly discovering crucial and admissible information while processing huge volumes of data. Businesses can use this to uncover insights that help them gain an advantage in the marketplace.    

4. Brand Loyalty with Customization

According to Retail Customer Experience, over 60% of consumers expect personalization. This challenge comes in the form of heavy expenses, as customization takes time and money. In order to deliver, companies are required to map out consumer journeys and predict offers that will increase user engagement and drive sales. It is a colossal task, and AI can help. 

Brands can use AI to predict what consumers will buy by analyzing data from previous purchases. Insights such as these allow businesses to deliver tailored content. AI can also be used to identify decision-making patterns and create consumer personas. Businesses can then drive specific content for each persona to better drive sales. 

5. Expansion

Executives can expand by deploying AI in their business models. For instance, data analysis offers growth opportunities for businesses in different areas. According to a report by Marketing Platform, data-driven business strategies can increase company revenue by 20% and reduce costs by 30%.

A good example of AI-driven business expansion is autonomous vehicle companies utilizing data to open revenue streams in insurance. Autonomous vehicles remove the need for drivers. As a result, the insurance liabilities currently in place need altering and adjustments accordingly. The shift of liabilities and risks changes how vehicles are insured, changing the costs of insurance and creating opportunities for innovation.          

6. Monitoring and Reduction of Human Error 

Businesses can implement immediate monitoring capabilities with the help of AI and its ability to process massive sums of data in real-time. AI’s monitoring capabilities alert businesses of potential issues, recommending appropriate action and perhaps even initiating a response.  

AI monitoring can also reduce human error and maintain established business standards. It does so by using available data to make decisions without emotions or opinions. While doing repetitive and mundane tasks, human workers are prone to making errors. In addition to automating and enhancing repetitive rule-based tasks, engineers can also train AI to improve upon itself and take on broader tasks as time goes on.

7. Talent Management

From rooting out corporate bias to streamlining the hiring process, businesses can use AI to improve various sectors of talent management. This not only saves hiring costs but also positively impacts workforce productivity. AI can successfully source, screen, and identify top-tier candidates, retain high performers and ensure equitable pay.   

If you want to read more about why AI is the gateway to a future-proof career, have a look at this blog: 4 Reasons Why AI is the Gateway to a Future-Proof Career. 

8. Predicting Outcomes

Another AI in business advantage is data-based outcome prediction. AI recognizes patterns in data, which businesses can use to determine how likely it is that a product will sell and in what volume. AI can also predict product-demand falls, helping companies purchase the right stock in correct volumes.

9. Optimize Logistics

Big data-driven AI applications streamline logistics on a large scale. It does so through AI-powered image recognition tools, among other processes. Such tools can help in the monitoring and optimization of business infrastructures, planning transport routes, and more. From improving supply-chain reliability and integrity to intelligent and automated warehousing, AI-powered logistics optimization can aid in the advancement of industries, such as travel and hospitality, healthcare, autonomous vehicles, infrastructure maintenance, and road safety, among many others.     

10. Increase Output and Prevent Outages 

AI can help increase product output, especially in manufacturing. It can automate business production by integrating industrial robots. AI can also be taught to perform labor-intensive or mundane tasks. It can, similarly, also be used in anomaly detection, especially in IT. This allows businesses to formulate early solutions and deter security intrusions.  

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 help engineers become leading AI industry experts and aid them in achieving a fulfilling and ever-growing career in the field. 

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Evolving leaps and bounds from when Alan Turing first explored the mathematical possibility of AI, this enigmatic field of computer science has become an integral part of today’s world at large. This fascinating field is progressing rapidly into diverse sectors of modern civilization.     

We interact with AI in many ways, whether with Siri and Alexa, or through other forms like smartphones, social media feeds, and music and media streaming services. Currently, more and more organizations are putting assets into Machine Learning. Many are also demonstrating a powerful development in AI items and applications.

What is AI?

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines. The engineers program the machines to mimic human actions- such as prediction and decision making. One of the core characteristics of an Artificially Intelligent machine is its capacity to rationalize and take actions that procure the best chance of obtaining a specific goal.

According to B.J Copeland, professor of philosophy and director of the Turing Archive for the History of Computing, University of Canterbury,

“Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings.”   

Hence, Artificial Intelligence intelligently executes tasks that capitulate in creating extensive accuracy, flexibility, and productivity for the entire system. In like manner, AI Engineers also navigate the inner workings of the organization to create and deploy practical models for real-world use. To know more about the responsibilities of an AI Engineer, you can check this blog- Responsibilities of an AI Engineer.    

Subsets of Artificial Intelligence (AI)

There are a plethora of methods that fall under the space of Artificial Intelligence, for example- linguistics, bias, vision, robotics, planning, natural language processing, decision science, etc. Let us look into 5 major subsets of Artificial Intelligence:

• Machine Learning (ML)
• Neural Network (NN)
• Deep Learning (DP)
• Robotics
• Computer Vision (CV)

Machine Learning (ML)

Modern computer programs can automatically adapt to, and learn from, new data without human assistance. This is what Machine Learning is. This process functions through Deep Learning techniques. Computer programs can automatically learn by absorbing large amounts of unstructured data such as images, texts, or videos. ML is perhaps the most applicable subset of AI to the average enterprise today.  

Neural Network (NN)

The Neural Network (NN) is a part of Artificial Intelligence that utilizes nervous system science (a piece of biology that deals with the nerve and nervous system of the human cerebrum) to merge the aspects of cognitive science with machines so they can perform tasks. Neural Network and Machine Learning combinedly tackle numerous intricate tasks effortlessly. 

Deep Learning (DP)

Chris Nicholson, CEO of Pathmind, offers a valuable analogy: Think of a lot of Russian dolls settled within one another. “Deep Learning is a subset of Machine Learning, and Machine Learning is a subset of AI, which is an umbrella term for any computer program that accomplishes something smart.”

Deep Learning brings into service the alleged neural systems, which learn from processing the labeled information provided during training. It then uses this answer key to realize what attributes of the information are expected to build the right yield. Deep Learning powers product and content recommendations for Amazon and Netflix. Furthermore, it also works in the background of Google’s voice-and image-recognition algorithms.

Robotics 

Robotics is a fascinating division of the innovative field. This subset of Artificial Intelligence focuses on the design and development of robots. Robotics is an interdisciplinary field of science and engineering that combines aspects of mechanical engineering, electrical engineering, computer science, and numerous other fields. It decides the production, design, operation, and use of robots. Additionally, it also manages computer systems for their control, intelligent results and data change.

Computer Vision (CV)

According to IBM– Computer Vision is a field of Artificial Intelligence that enables computers and systems to derive meaningful information from digital images, videos, and other visual inputs. Computers can then take actions or make recommendations based on that information. If Artificial Intelligence enables computers to think, Computer Vision enables them to see, observe and understand. 

AI Applications 

There are many sectors that use AI. Artificial Intelligence and like programs are extensively used for research in the medical field to improve the accuracy of programs that detect health conditions and in the creation of innovative technologies such as autonomous vehicles. 

Artificial Intelligence is also used in popular programs such as Netflix and Spotify. This type of AI monitors user habits and makes recommendations based on recent activity. Banks use AI systems to monitor member activities to check for identity theft, maintain online security and approve loans. One can find such AI systems in call centers as well. The programs analyze a caller’s voice in real-time to provide information to the call center to help build a faster rapport. 

Artificial Intelligence is creating advanced technologies in various fields which in turn creates a more efficient world. When you look at the environment today, you will find that AI-enabled machines are involved in various roles involving transportation, medical procedures, military applications, and even industrial and commercial fields. 

As such, the field of Artificial Intelligence is extremely lucrative and offers many career opportunities. To know more about the skills required to become an AI Engineer, you can check this article- Skills Required to become an AI Engineer.  

Conclusion 

There is no scarcity of challenges that need to be solved today to enable a better tomorrow for our society and the planet. Collaboration between humans and Artificial intelligence can lead to solutions that otherwise wouldn’t have been thought of. These solutions then can be developed and vetted at a pace that wouldn’t be possible if only humans were working on it without AI. Proper use of Artificially Intelligent machines can provide innovative ways of solving extremely challenging problems. It can also provide ways of significantly improving life. AI Engineers are always looking for innovative ways to produce ingenious products that enhance our lives. Check this article that elaborates on what AI Engineering is- What is 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 What is AI? | Artificial Intelligence and It’s Subsets 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.

What is AI?

The core concept of artificial intelligence is building machines with human intelligence. It is a technology that enables machines and computers to behave intelligently, perceive, learn, reason, and make decisions.

Based on the capability to replicate human characteristics and the techniques it uses to achieve them, AI can be categorized into 3 types:

Narrow AI (ANI), mostly referred to as weak AI, is programmed to perform a single task such as facial recognition, speech recognition/voice assistants, and search engine page results. Some examples of ANI are Siri by Apple, Alexa by Amazon, and Rankbrain by Google.

Strong AI/Artificial General Intelligence or deep AI, refers to the ability of a machine to imitate human behaviors, learn, and apply intelligence to solve problems that require human intelligence. Strong AI is demonstrated by Fujitsu’s K supercomputer but due to several inefficiencies, we can’t conclude strong AI can be achieved in the near future.

Artificial Superintelligence (ASI) is a step further from AGI, where artificial intelligence exceeds human capabilities and operates at a genius level. While ASI is still hypothetical, there are no limits on what it could accomplish. 

Subsets of AI

Most of the recent development in AI has been driven by machine learning and deep learning. AI is often referred to as ML or DL but the four common subsets/types of AI are machine learning, deep learning, computer vision and natural language processing.

Machine learning (ML) involves the use of statistical algorithms to enable machines to learn patterns from data. Its can be further categorized into supervised and unsupervised learning.

Deep learning (DL) is a subfield of machine learning that uses neural networks to find patterns from data. Neural networks are the interconnected structures of nodes aimed to simulate the operations of a human brain.

Computer vision (CV) involves creating a digital system that processes, analyzes, and understands based on visual data (images or videos). 

Natural language processing (NLP) is the process of converting text to speech and vice versa, enabling computers to understand, interpret and manipulate human language.

How AI works?

Machines (computers) learn from training data with the help of mathematical algorithms to make predictions. It ingests massive volumes of data to create a model. This model then identifies the patterns, analyzes data to reach a possible logical conclusion without human intervention. This automation of machine learning helps save time, reduces human error, and makes decisions. 

Computer vision algorithms break single images into multiple pixels to understand the detailed features, process, label and build a pattern to decode individual objects. NLP works by helping machines process and understands language so they can automatically perform repetitive tasks such as machine translation, summarization, and more.

Applications of AI

The use of AI is soaring every day as businesses across the globe are leveraging it to optimize operations, products, and services.

Chatbots have dramatically reduced data collection time for all businesses, accelerating data processing and providing data-driven insights to increase ROI.  Chatbots use NLP to mimic human conversations and identify the logic/reason behind these conversations, directing it towards the possible solution by learning from the past real conversations.

AI tools are being used in agriculture to help farmers control weeds and harvest faster whereas AI in e-commerce is gaining traction with such companies leveraging ML and NLP for product recommendations, customer information and feedback inventories, chatbots and personalized shopping experiences.

AI in Healthcare

Sophisticated machines or surgical robots can efficiently diagnose and reduce errors in medical labs. AI models using algorithms and deep learning are able to detect early symptoms of cancer. Radiologists at Zebra Medical Vision use AI-enabled assistants to analyze scanned images to make diagnoses. AI uses historical data and medical intelligence in the research and discovery of new drugs as well.

AI in Education

AI is enhancing educational tools and digitizing institutions. Schools are using AI-enabled learning tools and features to fill gaps in teaching and make learning more student-centric. Tools such as automated assignment grading and progress monitoring save teachers lots of time. Automation of redundant administrative tasks is yet another example of how AI is helping the education sector.

Experts claim the future of work is likely to revolve around AI. 40% of respondents from Global 2000 organizations say they are adding more jobs as a result of AI adoption (Dun & Bradstreet 2019). Despite the many opportunities, there is a huge AI talent supply gap. We need to democratize AI and create opportunities for students to engage with AI early on. Learning AI is challenging and requires advanced degrees and high-quality programs.

Fusemachines’ Fuse.ai center has been offering beginner to advanced AI courses to learners from underserved communities. With the mission to make AI accessible to everyone, we provide certified programs – from Foundations in AI to a MicrodegreeTM in AI and other specialization courses suitable for AI enthusiasts of all levels. These courses develop a good understanding of AI,  and enhances confidence in learners to pursue a career in AI.

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