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.

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.

The post Quantum Computing and AI appeared first on Fuse AI.

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.

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

What is the Ethics of AI? 

AI ethics are techniques, values, principles, and accepted standards of right and wrong to guide moral conduct in development and deployment. 

Robot ethics

Robot ethics, or roboethics, refers to the morality of how humans build, design, use and treat robots. This subset is concerned with the rules AI engineers and those involved in the creation and deployment of AI models should apply to ensure ethical robot behavior. Roboethics deals with moral dilemmas, such as concerns of robots posing threats to humans or using robots in wars.

The main principle is guaranteeing autonomous systems exhibit acceptable behavior in situations with humans, AI systems, and other autonomous systems such as self-driving vehicles. Robot ethics emerged out of engineering ethics and shares its origins with both Asimov’s Laws and traditional engineering concerns of safe tools. 

Machine ethics

Unlike roboethics, machine ethics, also known as machine morality, is a new field that focuses on the designing prospects of computer and robotic systems that demonstrate sensitivity to human values. In other words, machine ethics deals with the implementation of human value sensitivity into AI models so that they can make morally sound decisions. As such, this field is concerned with designing Artificial Moral Agents (AMAs), robots, or artificially intelligent computers that behave morally.

You can think of a robot’s choices and actions as hard-wired. We sometimes refer to this as “operational morality.” As systems become more autonomous, there arises the need to build AI models with ethical routines so that they can select and act out appropriate behavior from among the various courses of action. This is known as “functional morality,” and this is what Machine ethics is about. Functional morality can still fall far short of full moral agency. 

Click here to read the blog about the ethical concerns of AI: 6 Major Ethical Concerns with AI.

Ethics of AI Principles 

Governments, the EU, large companies like Microsoft and Google, and many other associations have drafted several policy documents and ethical guidelines related to the ethics of AI over the years. The converging result currently presents 11 major ethical principles:  

Transparency

Transparency is the most prevalent principle in current AI ethics literature. Common things include increased explainability, interpretability, or other acts of communication and disclosure. After all, the impact of AI in people’s daily lives will grow the more it is applied, potentially in life or death decisions, like the diagnosis of disease and illnesses, or the choice of self-driving cars in complex traffic situations. This thus calls for high levels of transparency.  

We can apply this principle in data use, human-AI interaction, and automated decisions. Transparency in AI allows humans to see, understand and explain if the models have been thoroughly tested. We can also understand why AI made particular decisions, and what data the AI model has ingested. This helps answer such questions as “What that decision was based on?”, and “Why was it taken the way it was taken?” 

Transparency also helps minimize harm, improve AI responsibility, and foster trust. After all, transparency in AI helps make underlying values definitive and encourages companies to take responsibility for AI-based decisions. Such responsible decisions will then not exclude ethical considerations while aligning with the core principles of the company.

Many policy documents suggest increased disclosure of information by AI developers and deployers, although specifications regarding what should be communicated vary from one policy to the next, with some asking for transparency regarding the AI source code, limitations of AI models, and investment specifics while others ask for transparency regarding the possible impacts of AI systems.   

Justice, Fairness, and Equity

Many ethical guidelines call for justice and the monitoring of bias. There are some sources, such as the Position of Robotics and AI policy by Green Digital Working Group, that also focus on justice as respect for diversity, inclusion, and equality. This ethical principle of AI emphasizes the importance of fair access to AI and its benefits, placing a particular emphasis on AI’s impact on the labor market and the need to address democratic or societal issues. 

Non-maleficence

The principle of non-maleficence calls for safety and security, stating that AI should never cause foreseeable or unintentional harm. More considerations entail the avoidance of specific AI risks or potential harms, such as intentional misuse via cyber warfare or malicious hacking. Risk-management strategies also fall under this principle, such as technical measures and governance. Such strategies can range from interventions at the level of AI research and design to technology development and/or deployment. 

Responsibility and Accountability

Sources rarely define responsibility and accountability in AI, despite widespread references to “responsible AI.” Recommendations include acting with integrity and clarifying the attribution of responsibility and legal liability. This principle also focuses on the underlying reasons and processes that lead to harm. 

Privacy

Privacy is seen as a value to uphold and a right to be protected. This is often presented in relation to data protection and data security. Hence, in order to uphold privacy, suggested modes of achievement fall into four categories: technical solutions, such as differential privacy and privacy by design, data minimization, access control, and regulatory approaches. 

Beneficence

The principle of beneficence comprises the augmentation of human senses and promotion of human well-being, peace, and happiness. This ethical principle focuses on the creation of socio-economic opportunities and economic prosperity. Strategies for the implementation of this principle include aligning AI with human values, advancing scientific understanding, minimizing power concentration and conflicts of interests.  

Freedom and Autonomy

This refers to the freedom of expression and the right to flourish with self-determination through democratic means. This ethical philosophy also refers to the freedom to use a preferred platform or technology. Transparency and predictable AI promote freedom and autonomy. 

Trust

This principle calls for trustworthy AI research and technology, trustworthy AI developers and organizations, and trustworthy design principles. The term also underlines the importance of customers’ trust. A culture of trust among scientists and engineers can support the achievement of other organizational goals. Furthermore, in order for AI to fulfill its potential, overall trust in recommendations, judgments, and AI use is indispensable.    

Education, reliability, and accountability are important to build and sustain trust. Engineers can also develop processes to monitor and evaluate the integrity of AI systems over time. Additionally, while some guidelines require AI to be transparent and understandable, others explicitly suggest that instead of demanding understandability, AI should fulfill public expectations.

Sustainability

Sustainability calls for the development and deployment of AI to improve the ecosystem, protect the environment, improve biodiversity, and contribute to fairer and more equal societies. Ideally, AI can create sustainable systems whose insights remain valid over time through the increase in efficiency in the process of designing, deployment, and management of AI models. We can achieve sustainability by minimizing the ecological footprint. 

Dignity

The principle of dignity is intertwined with human rights. It entails avoiding harm, forced acceptance, automated classification, and unknown human-AI interaction. Artificial Intelligence should not diminish or destroy but respect, preserve and increase human dignity. 

Solidarity

Solidarity is an ethical principle mostly referenced in relation to the implications of AI for the labor market. It calls for a strong social safety net. This fundamental philosophy underlines the need for redistributing the benefits of AI to protect social cohesion and respect vulnerable groups.

Why do we need Ethics of AI? 

Ethical AI ensures that AI initiatives maintain human dignity and don’t cause harm. Data often reflects the bias in society, and when not corrected, can cause AI systems to make biased decisions. AI firms need to ensure that the choices they make, from the partners they work with and the composition of their data science teams to the data they collect, all contribute to minimizing bias. Furthermore, the adoption of ethical AI principles is essential for the healthy development of all AI-driven technologies. Self-regulation by the industry will also be much more effective than any legislative effort if engineers and developers uphold ethical principles during the creation and deployment process.  

You can also read our previous blog about how businesses benefit from AI: 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. 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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Similarly, AI breakthroughs and developments in Machine Learning (ML) will also continue to push boundaries, similar to how steam, electricity, and computers ushered in the first three industrial revolutions. 

Here are the top 22 AI trends in 2022.

AI Engineering

AI Engineering will be at the forefront of future AI trends. The staying power and lasting value of AI investments have been tremendous across many companies. In like manner, as the market for AI innovations grows, efforts into AI models will also expand to drive investments. In fact, future trends lean towards the mass adoption of AI engineering, leading to three times the value for AI efforts.  

You can also check our previous blog to know why AI Engineering is one of the most high-in-demand career prospects in the market today: What is AI Engineering and Why You Should Join this Field.    

Web 3.0

Web 3.0, or “Semantic Web,” is where the web will be used as a database incorporated with intelligent search engines, efficient filtering tags, and digitized information. Consisting of AI-enabled services, Web 3.0’s decentralized data architectures and edge computing will make it one of the biggest AI trends in 2022. 

AI in Healthcare

The healthcare industry is among the primary economic sectors that will continue to evolve as Machine Learning and AI in technology become more prevalent. Current AI trends already include AI-enabled machines being as good as human experts when it comes to diagnosing disease from medical images. Moreover, current Deep Learning software also show enormous promise in diagnosing a range of diseases, including cancer and eye conditions.

AI trends in 2022 in healthcare will include researchers developing AI models that can predict the development of breast cancer years in advance. Crucially, the system will be created to work well for diverse patients. Similarly, another trend that can quickly become a global standard in the near future is Infervision’s image recognition technology that will use AI to look for signs of lung cancer in patient scans.

AI in Cybersecurity

Hacking and cybercrime have inevitably become more of a problem as machines take up more of our lives. Every device connected to a network inevitably becomes a potential point-of-failure that hackers could exploit. As a matter of fact, the World Economic Forum identified cybercrime as potentially posing a more significant risk to society than terrorism.

It is a given then that potential AI trends in 2022 and beyond will focus on cybersecurity. Identifying points of failure becomes more complex as networks of connected devices become more complex, and this is where AI can play a role. Smart AI algorithms will play an increasingly major role in keeping cyber-crimes at bay by analyzing network traffic and learning to recognize patterns that suggest nefarious intentions.

Simultaneously, a significant AI application in cybersecurity in 2022 includes the Cybersecurity mesh. It is a form of architecture that provides an integrated approach to IT security assets no matter the location. It will consequently redefine the perimeters of cybersecurity as it will provide a more standardized and responsive approach to people’s identities or things. This is a pathway to reduce the financial implications of cyber incidents by almost 90%.

Hyper Automation

Automation enables technologies to produce and deliver goods and services with minimal human intervention. The current implementation of automation in technologies and techniques has already improved the efficiency, reliability, and speed of tasks previously performed by humans. As such, automation is critical for digital transformation. 

Likewise, Hyper Automation means faster identification and automation across enterprises. It will improve work quality, hasten business processes, and foster decision-making. Thus, as new innovations emerge, Hyper Automation will be on the rise, which is why it is one of the growing AI trends in 2022.

Augmented Workforce

Many companies embrace the process of creating data and AI-literate cultures within their teams. As time goes on, this will become the norm, with the human workforce working with or alongside machines with cognitive functionality. 

In many sectors, AI-enabled tools are already used to determine leads that are worth pursuing. The tools also convey the value businesses can expect from potential customers. For example, in engineering, AI tools provide predictive maintenance. Likewise, in knowledge industries such as law, AI-enabled tools help sort through a growing amount of data to find valuable information.

Generative Artificial Intelligence (AI)

Generative AI algorithms use existing content, such as text, audio files, or images, to create new content. In other words, it enables computers to use abstract and underlying patterns related to the input to generate similar content. There has been an increase in interest and investment in generative AI over the past year. By the same token, predictions include generative AI accounting for 10% of all data production in the next three and a half years, a significant increase from the current 1%. 

AI in Entertainment  

Current AI-enabled content platforms, such as Netflix and Spotify, use AI to understand what viewers want to watch or listen to and make personalized recommendations. As new AI-enabled innovations emerge, more of such similar tools and services will become popular. Some examples of trendy AIs in the entertainment sector include search engines, such as China’s Sogou, capable of creating an AI that can read novels aloud, simulating the author’s voice (similar to how Deepfakes can create realistic audio and video content). Other examples include AI-enabled tools, such as Sony’s AI DrumNet, which produces drum beats.

Data Fabric

A data fabric is an architecture that serves as an integrated layer (fabric) of data and connecting processes that provide consistent capabilities across a choice of endpoints spanning hybrid multi-cloud environments. In adjacent, it also standardizes data management practices across cloud and devices, fostering resilient and flexible data integration across platforms. Additionally, the standardization can also lead to significantly reduced data management efforts while also substantially improving time to value. 

Better Language Modeling

Language modeling allows machines to understand and communicate with humans in our spoken languages. Simultaneously, it enables the translation of natural human languages into computer codes that can run programs and applications. An example is GPT-3 by OpenAI, the most advanced language model ever created. This model consists of around 175 billion parameters (variables and data points machines use to process language). A future AI trend includes OpenAI’s successor, the GPT-4, predicted to be even more powerful with 100 trillion parameters, making it 500 times larger than GPT-3. 

Intelligent Consumer Goods 

Smart consumer goods aim to simplify mundane tasks by getting to know one’s preferences and behavior to anticipate needs and respond accordingly. It works similarly to AI-enabled tools and services that fall within the entertainment sector. Examples of smart consumer goods include Google’s Nest’s thermostat, which tracks how people use their homes so that it can regulate the temperature. Similarly, the Orro intelligent light switch can detect when someone enters a room and switch the lights on and off. 

Autonomic Systems

Autonomic systems with built-in self-learning can dynamically optimize business performance, and protect against cybercrimes. This trend anticipates greater levels of self-management of software.

AI and the Metaverse

A unified digital environment, the metaverse, is a virtual world much like the internet, where users can work and play together. It emphasizes enabling immersive experiences often created by users themselves. AI will be a significant player in the metaverse, helping create online environments where humans can nurture their creativity. An example of a metaverse is depicted in the 2021 Ryan Reynolds movie “Free Guy.” 

Decision Intelligence (DI)

Decision Intelligence is a discipline of AI engineering that augments data science with social science, decision theory, and managerial science. DI applications provide a framework for best practices in organizational decision-making. It also aims to hasten decision-making by modeling decisions in a repeatable way to increase efficiency. It’s predicted that one-third of large enterprises will use DI for better and more structured decision-making in the next two years.

IoT in Business

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals, or people provided with unique identifiers (UIDs). These interrelated units have the ability to transfer data over a network without human-to-human or human-to-computer interaction. The IoT allows businesses and companies to make and sell products by making them smart and delivering unprecedented insights into product use. These insights allow companies to deliver better services and products. 

The IoT gives businesses the chance to deliver customer value propositions and generate income streams. Data generated from IoT devices are a vital business asset and can bolster a company’s value. For many companies, the most prominent IoT opportunities are data generated from smart machines. The data can improve company operations and reliability, and reduce costs.

Read our previous blog about how AI applications can benefit businesses: 10 Benefits and Applications of AI in Business.

Composable Applications

Composable applications highlight functional blocks of an application that can be decoupled from overall applications. These individual parts can be more finely tuned to create new applications. Companies that can leverage composable applications are predicted to outpace the competition by 80% regarding feature implementation, making it one of the notable AI trends in 2022 in business.

Low-code and No-code AI

No-code and low-code solutions offer simple interfaces to bypass the AI talent demand gap. These interfaces can be used to construct increasingly complex AI systems. No-code AI systems will enable the creation of smart programs by plugging together premade modules. These modules can then be fed domain-specific data, much like how web design and no-code UI tools, such as Wix or Squarespace, let users create web pages and other interactive systems by dragging and dropping graphics. Natural Language Processing (NLP) and Language Modeling may make it possible to use voice or written instructions to create programs. This will play a vital role in the democratization of AI and data technology.

Cloud-Native Platforms (CNPs)

Cloud-Native Platforms will provide the foundation for most digital initiatives by mid-decade. These platforms leverage cloud technology to offer IT-related capabilities. Subsequently, they reduce vendor lock-ins by giving users a choice of tools without being stuck with legacy offerings. Cloud-Native Platforms are more portable and beyond the reach of predatory vendor pricing as they run on multi-cloud compatible tooling. Invisible infrastructure equals easy portability. Using the cloud for storage offers access to files from anywhere with an internet connection. Files can still be accessed in the event of a hard drive failure or other hardware malfunction. CNPs act as a backup solution for local storage on physical drives.  

Autonomous Vehicles

AI will guide autonomous cars, boats, and aircraft set to revolutionize travel and society over the coming decade. Tesla says its cars will demonstrate full self-driving capability by 2022. Accordingly, we can expect competitors Waymo, Apple, GM, and Ford to announce significant leaps forward in the next year. 

Privacy Enhancing Computation (PEC)

Data and information privacy is an increasingly important concern. Privacy Enhancing Computation (PEC), accordingly, protects the confidentiality of a company and its customers’ data. Reducing privacy-related risks consequently helps maintain customer loyalty. As a matter of fact, it is estimated that roughly 60% of large enterprises will leverage PEC practices by 2025. 

Creative AI

We have used AI to create art, poetry, music, plays, and even video games. Popular examples include the paintings of Pindar Van Arman and the music of Taryn Southern. Moreover, we can expect even more elaborate and fluid creative outputs as new models, such as GPT-4 and Google’s Brain, redefine boundaries. We can similarly expect to see AI applied to routine creative tasks, such as writing headlines for articles and newsletters and designing logos and infographics. 

Non-Fungible Tokens (NFT) 

A Non-Fungible Token (NFT) is a non-interchangeable unit of data saved on a digital ledger (blockchain). NFTs can be used to represent reproducible items such as photos, videos, audio, and other types of digital files, as unique items. In the same way, 2022 will see companies dabbling in the creation of NFTs for a fee. We have already seen this in the arts and entertainment. We will also likely see the emergence of more tokenization marketplaces.

In conclusion, the fourth industrial revolution offers enormous opportunities to make the world a better place. Equally important is the proper use of these technologies. They can address some of the world’s biggest challenges – from climate change and inequality to hunger and healthcare. As a result, these technologies will change businesses, reshape business models, and transform industries. 

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As a result, there is a high demand for data scientists and analysts in the market, with salaries above the national average. The World Economic Forum Future of Jobs Report 2020 also lists these roles as number one for increasing demand across industries. 

So data analyst vs data scientist- what is the difference? Well, both data analysts and data scientists work with data. The difference is what they do with it. 

What Does a Data Analyst Do?

The first thing to remember is that the responsibilities of a data analyst vary between industry and company. But fundamentally, a data analyst identifies trends by gathering data to help businesses make strategic decisions. Firstly, they analyze data sets to answer important business questions, such as why a given quarter saw a drop in sales, why certain marketing campaigns fared better in certain regions, and how internal attrition affects revenue, among others.  

Secondly, data analysts also perform statistical analyses to solve problems. Subsequently using tools and programming languages such as SQL, R and SAS, Power BI, and Tableau among others, data analysts make database queries and clean or convert data into usable formats by discarding irrelevant information. Likewise, data analysts also deal with missing data using different methods based on the type of missing data and available datasets.  

Similar to many other work teams, data analysts, with a range of fields and titles, also typically work in a company’s interdisciplinary team. A data analyst’s many titles include database analyst, market research analyst, business analyst, financial analyst, operations analyst, customer success analyst, pricing analyst, and international strategy analyst.  

Likewise, skills include technical expertise and the ability to communicate quantitative findings to non-technical colleagues or clients. Moreover, data analyst talents also include data mining/data warehousing, modeling, statistical analysis, and database management. 

What Does a Data Scientist Do?

To begin with, a data scientist develops tools and methods to extract organizational information to solve complex problems. In other words, a data scientist designs data modeling processes and creates algorithms and predictive models to automate systems and data frameworks. Consequently, data scientists also make business estimations by writing algorithms and building statistical models. They do so by gathering, cleaning, and processing raw data. Moreover, data scientists also design predictive models and Machine Learning algorithms to mine large data sets.

Additionally, the role of a data scientist also includes arranging cluttered data sets using multiple tools simultaneously to build automation systems and frameworks. Thus, data scientists require business intuition and critical thinking to understand data’s implications.  

Similarly, a data scientist possesses mathematical and statistical knowledge as well, and uses these skills to approach problems in innovative ways. In particular, data scientists must have skills in Machine Learning (ML), software development, Hadoop, Java, data-mining/data-warehousing, data analysis, Python, and object-oriented programming.

In like manner, data scientists are also responsible for the development of tools used to monitor and analyze data accuracy. By the same token, they also build data visualization reports and automate the data collection process. 

Skill Comparison 

Differences

In conclusion, when it comes to the difference between a data scientist vs data analyst, the data scientist is the one responsible for creating and maintaining the tools necessary to crawl and collect the data. A data analyst then examines that data to make important predictions. Both simultaneously play an important role in aiding businesses to leverage the full potential of AI. 

Besides, if you want to read about how applications of AI can help modern businesses, you can check out our previous 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. The proprietary curriculum, likewise, includes courses in Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision. These certifications consequently help engineers become leading AI industry experts. 

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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.

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“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.

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