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

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

What is AI?

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

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

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

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

Subsets of Artificial Intelligence (AI)

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

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

Machine Learning (ML)

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

Neural Network (NN)

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

Deep Learning (DP)

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

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

Robotics 

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

Computer Vision (CV)

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

AI Applications 

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

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

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

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

Conclusion 

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

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

The post What is AI? | Artificial Intelligence and It’s Subsets appeared first on Fuse AI.

Similarly, while AI Engineering is a relatively new field, it is becoming more and more relevant in many economic sectors, especially in the industrial and manufacturing industries. Not only that, but AI Engineering is also becoming relevant in healthcare, security, finances, and entertainment. 

If you want to know more about why AI Engineering is important today, check out this article: What is AI Engineering and Why Should You Be One.  

Some of the most interesting and meaningful applications of AI are in engineering and more and more people are entering the field. 

Roles and Responsibilities of an AI Engineer 

In fact, AI Engineers are responsible for using programming algorithms so they can assemble, test, and convey AI models. Likewise, AI Engineers are problem-solvers; it is their responsibility to ensure that AI systems or infrastructures are properly implemented. 

AI Engineers build, deploy and maintain AI-based systems. Working closely with businesses and tech companies, AI Engineers help improve company operations, enhance products or software development, and make proper service delivery, among others. 

Furthermore, if you want to learn more about the technical skills required to become an AI Engineer, check out this article, Skills Required to Become an AI Engineer.  

Similarly, AI Engineers are primarily responsible for implementing Machine Learning (ML), a process that involves the application of Artificial Intelligence to automatically perform a specific task without explicitly programming it. 

Other obligations include: 

• Creating and managing the AI development process and the overall infrastructure of the product
• Directing factual investigation and deciphering outcomes so that they can guide the organization’s decision-making process
• Mechanizing framework for the data science team 
• Creating foundations for information change and ingestion 
• Building AI models 
• Explaining their AI models to stakeholders
• Changing the AI models into APIs different applications can communicate with

In addition, aside from the responsibilities above, the core responsibility and duty of an AI Engineer is to navigate through the AI Framework. For the most part, AI Engineers focus on learning how to build and deploy AI models. However, the most essential skill to master is the complete understanding of AI/ML workflow. This workflow system includes- 

• Data Preparation
• AI Modeling
• Simulation 
• Testing and Deployment 

The AI-Driven Workflow 

Correspondingly, as an AI Engineer, you need to know that AI is not just modeling, but also a series of steps: data preparation, modeling, simulation and test, and deployment.

Step 1: Data Preparation

Firstly, Data preparation is the process of feeding accurate data as input to train a model. If engineers do not provide accurate data, projects are more likely to fail, and engineers will not get accurate or helpful results. To train a model, AI Engineers should begin with large amounts of clean, labeled data. This is also one of the most time-consuming steps of the workflow.

An AI model is likely to fail without accurate training data. AI Engineers should know data classification procedures, the clustering process, the task of regression, human insights, data cleansing activities, and discretion proceedings.   

Step 2: AI Modeling 

Secondly, after cleaning the data and properly labeling it, comes AI modeling. This is where data is used as input, and the model learns from that data. The goal is to create an accurate model that can make intelligent decisions based on data. This is also where Deep Learning, Machine Learning, or a combination thereof comes in.

AI Engineers must learn how to create accurate models that can make decisions based on data. For this, AI Engineers would use the concepts of Deep Learning (Neural Networks), and Machine Learning (Decision Trees). 

Step 3: Simulation 

The model does not exist in a vacuum and must work with all other pieces in the system. Let us take an example of an automated driving scenario. Not only does the self-driving car have a perception system for detecting objects (cars, pedestrians, stop signs), but the vehicle’s perception also has to integrate with other systems for path planning, localization, controls, and more. 

The key to a successful model is proper simulation and testing. Indeed, it is only when an AI model passes the simulation and testing phase that it is deployed into the real world. 

Step 4: Deployment 

The final step is Deployment. Engineers need to master making an implementation-ready model that can be fitted into a designated hardware environment. Accordingly, once the model is ready to deploy, the next step is the target hardware. In other words, deployment is the step in which the AI Engineer readys the model in the final language in which it will be implemented. In this step, the design engineers share an implementation-ready model which fits into the designated hardware environment. The designated hardware environment can range from desktops to the cloud to FPGAs.

Conclusion 

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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How to Become an AI Engineer

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

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

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

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

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

Education Prerequisites Required to be an AI Engineer 

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

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

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

Technical Skills Required to Become an AI Engineer

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

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

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

Programming Skills 

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

Applied Math and Statistical Skills

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

• Gaussian Mixture Models
• Naive Bayes
• Hidden Markov Models

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

Natural Language Processing Skills 

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

Deep Learning and Neural Networks Skills 

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

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

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

Conclusion 

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

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

Tech giants such as Google, Microsoft, Apple and Amazon are pouring in billions of dollars into AI products, services and talent. With the right guidance and skills, one can master this hot field.

Become an AI engineer by mastering these 5 skills

1. Get a bachelor’s degree in a relevant field

The first step to becoming an AI engineer is having a basic graduate-level education qualification in a relevant field such as Mathematics, Statistics, Computer Science, Information Technology, Economics or Finance. A bachelor’s degree is enough for a student to start their journey in an entry-level position. However, if he/she wants to be in the competitive AI job market, a master’s degree in Computer Science with a specialization in AI or ML courses is crucial. One can also aim to achieve PhD certification in specialized areas including Machine Learning, Deep Learning, Computer Vision and Natural Language Processing. 

2. Brush up your programming and statistical skills

Programming, statistics, and math knowledge are other essential AI engineer skills. Start by learning programming languages such as: 

•  Python
• Java,
•  R
• C/C++
• Lisp
• Prolog 

It’s imperative to have a thorough understanding of programming concepts such as:

• Classes
• Data structures
• Static methods
• Recursion and loops
• OOPS (Object-Oriented Programming System)
• Software development life cycle and
•  Libraries. 

Quick tip: Start by practicing Python as most machine learning engineers use this language because of its easy-to-learn syntax, versatility and large library. 

For statistical and applied maths skills, one needs to be familiar with calculus, matrices, derivatives, and understand models such as Naive Bayes, Gaussian Mixture and Hidden Markov Models. Engineers must have in-depth knowledge of algorithm theory and how they work. Successful ML engineers should have experience solving Gradient Descent, Lagrange, Quadratic Programming, Partial Differential equations, and so on.

3. Learn basic AI frameworks

Most of the time, AI engineers focus on learning how to build and deploy AI models. But the most important skill to master is to have a complete understanding of AI/ML workflow which includes data preparation, AI modeling, simulation and testing and deployment. Without robust and accurate data to train with, an AI model is likely to fail. For this, one should learn processes such as data classification, clustering, regression, human insights, data cleansing, and discretion.  

The next step is AI modeling where one must learn how to create a robust, accurate model that can make decisions based on data. Engineers should start building the concepts of deep learning (neural networks), and machine learning (decision trees). The third step is simulation and accuracy testing where engineers validate that a model is working properly. For validation, engineers should learn tools such as Simulink. The final step is deployment. Engineers need to master making an implementation-ready model that can be fitted into a designated hardware environment. 

4. Experiment with data sets and tools such as TensorFlow and Keras

Now that you know the fundamentals of AI tools, programming languages, frameworks, it’s time to get hands-on and involved in mini-projects. Learn where you can find free public data sets that can be anything from hospitals, criminology department to Wikipedia and Google trend data sets. Get to know Kaggle Datasets, where most AI engineers hunt for publicly available datasets and even refer to other projects built with the same data set. Following this, engineers should also understand powerful reinforcement learning tools such as TensorFlow and APIs such as Keras. 

5. Learn business skills

In order to pitch the AI models and products to stakeholders, AI engineers need to think like business leaders and communicate well. That is why they are required to learn business skills such as critical thinking, problem-solving, leadership, project management, industry knowledge, and networking. Engineers need to explain AI and ML concepts to people with less knowledge about AI and should learn good communication skills, and solve problems quickly.

Some interesting AI jobs Facts and Statistics

• According to Gartner, AI is set to create 2.3 million jobs by the end of 2020, leading a net gain of 500,000 potentially new jobs. 
• According to International Data Corporation (IDC), the number of AI jobs is expected to globally grow 16 percent this year.
• IDC forecasts by 2024, the AI market is expected to break the $500 billion mark with a five-year compound annual growth rate (CAGR) of 17.5% and total revenues reaching an impressive $554.3 billion.
• According to Indeed, the Average Salary of an Artificial Intelligence Engineer is around $110,000 per Annum, with a minimum of $105,244 and a maximum of $144,611.
• Multinational companies such as Amazon, Google, Microsoft, NVIDIA, Facebook, Intel, Rocket Fuel, General Electric, Cylance, Oculus VR, Booz Allen Hamilton, Huawei, Adobe, Accenture, iRobot, Magic Leap, Rethink Robotics, BAE Systems, HERE, IBM, Samsung, Lenovo, MoTek Technologies, Uber, PCO innovation, Rakuten Marketing, and Wells Fargo are the leading employers who are hiring top AI engineers since 2018.

Engineers should consider taking certification courses to master the above skills. To be a successful AI engineer, completing a certification course in Data Science, Machine Learning or Artificial Intelligence is highly recommended. 

Fusemachines envisions democratizing AI and has been educating AI, targeting students at the K-12 level all the way through the graduate level, especially in underserved communities. To provide integrated AI courses, Fusemachines has partnered with multiple schools and colleges in Nepal, Latin America and other South Asian countries. The company has opened Fuse.ai center, which is an AI research and training center offering 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 this will help engineers become leading AI industry experts and envision a fulfilling and ever-growing career ahead.

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