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]. 

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Job Concerns

There are widespread concerns about jobs being lost due to AI automation, and while these concerns are valid, job loss doesn’t mean a scarcity of jobs. It is estimated that AI will create 97 million new jobs by 2027, and it is crucial to train for the job transformation in order to fill these new roles.

Though job loss is a concern, the shortage of AI talent is simultaneously stunting the ability of countries and organizations around the world looking to embrace transformation led by AI. Combined, these concerns point to a rising need to invest time and resources in educating and (re)training the workforce. 

According to a recent IBM report, around 120 million people around the world will need retraining to fit the job market. This includes 11.5 million people in the US. The current workforce will also need re-skilling in the coming years to support the shift towards AI and intelligent automation. 

AI Talent Shortage

Though there has been a 19% increase in AI specialists over the last year, the amount of AI experts remains alarmingly small given the growing prevalence of AI. 

The scarcity of AI expertise is an impediment to the technology’s adoption. Recent research reports that 56% of senior AI specialists say a shortage of new, competent AI personnel is the most difficult barrier to achieving AI application throughout business operations.

Bridging the AI talent gap

One strategy to overcome the AI skills gap is to invest in education for digital, math, and technological education. While increasing the number of STEM and computer science students will help, it won’t solve the problem entirely.

According to the World Economic Forum, more than half (54%) of all employees worldwide will need major reskilling by 2022. To eliminate the AI skill gap, companies must confront the issue head-on and invest in re-skilling their workforce. 

Bottom Line

There is no easy solution to the problem of AI skill shortage. Businesses must exercise caution and make the best use of limited resources while investing in strategies that will deliver long-term results rather than one-time remedies. More importantly, firms must plan think long term when it comes to recruiting, employing, and overseeing the newly arriving cohort of AI talent. 

Fusemachines AI Fellowship Program in Latin America is a comprehensive program to train and provide a platform for students of the region to launch their AI careers. Learn more about the program here.

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

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

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

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

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

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

• Solutions for AI Platforms

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

• Machine Learning (ML)

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

• Metrics and Big Data

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

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

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

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

• Objectives

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

• Data Protection Regulations

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

• Sources and Quantity of Data

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

• Recruit Data Science Experts

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

• Ensure Data Quality

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

Choosing a Machine Learning Platform

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

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

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

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

The post AI for Smart Marketing appeared first on Fuse AI.

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

Samsung and AI

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

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

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

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

Samsung’s Virtual Assistant Bixby

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

Bixby’s features include:

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

Household Robots

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

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

Smart TVs

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

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

Smartphone Cameras

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

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

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

Conclusion

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

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

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

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

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

A Brief Introduction to Quantum Computing 

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

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

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

Real-World Examples of Quantum Computers 

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

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

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

Quantum AI 

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

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

Why is Quantum AI Important?

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

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

Current Application of Quantum Computing and AI 

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

Processing Large Datasets

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

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

Solving Complex Problems

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

Business Insights and Models

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

Integrating Multiple Datasets

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

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

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

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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.

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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“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.  

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