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Artificial Intelligence (AI)

The Difference Between AI and Machine Learning?

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later.

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Will AI render Software Developers Obsolete

Do you ever read articles mentioning both Artificial Intelligence (AI) and Machine Learning (ML) and ask yourself how do these relate and how do they differ from each other? Don’t worry; you’re not alone in this struggle. Our global society is becoming increasingly digitised, and chatbots, speech processing devices and intelligent mobile app algorithms are all around us, so you better catch up with the trends. It is time to demystify the concepts of AI and ML and finally bring some clarity into the blurry picture.

From my practice working in a bespoke software development company, I hear and read about deep learning, neural networks and autonomous systems all the time. This article puts the main differences between those buzzwords in the spotlight and reveals what hides behind it all. Some companies only claim to use AI to market their products better, hoping that only true tech enthusiasts will notice the difference. While there are indeed lots of overlapping technologies behind AI and ML, here are the fundamental difference setting them apart:

1. The Core of AI and ML as Technologies

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later. Artificial Intelligence is a subsector of Information Technology (IT) that focuses on developing machine algorithms that simulate natural human intelligence and automating them to behave in a way that exerts intelligence.

The main areas that concern AI is learning from experience and efficient problem-solving. As John McCarthy, the father of AI, once put it, AI mimics human intelligence but is not constrained by biologically observable methods.

Machine Learning, on the other hand, is a subset of AI that concentrates on developing computer algorithms that get machines closer to learning like actual humans. Here is a Venn diagram to make it simpler. The recognition of distinct patterns and pattern regularities and the subsequent derivation of suitable solutions are the tasks that this technology masters.

An algorithm needs to be fed existing databases to learn to recognise and follow the patterns for this to happen. The ML generates artificial knowledge derived from its previous experience. All the knowledge that is gained can be generalised and applied for solving other problems. This approach allows previously unknown data to be processed and used quickly.

2. Types of Artificial Intelligence

Generally speaking, there are two main types of AI: weak (narrow) and strong (general). Most of the AI-powered software solutions we come across are weak ones, and it is called like that because it can only perform several tasks well, meaning it has limited functionality.

The narrow type of AI can successfully handle simple problem-solving tasks after the appropriate training period. Some of the main areas where narrow AI can shine are text, speech or image recognition, navigation systems, streaming services etc.

The second type of AI – the strong one, is yet to be fully developed. It refers to programming machines to perform complete tasks, requiring general intelligence and human-like consciousness. These types of futuristic robots will be able to think autonomously without special training. They will most like show some level of self-awareness, while it is not expected that narrow AI will reach such a cognitive awareness state.

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3. Types of Machine Learning

While we wait for this general AI type to become more than a far-fetched concept, let’s discuss the main subdivisions of ML, shall we? There are three core ML types: Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL). SL is when software developers instruct the algorithm to learn something, e.g. differentiate between a snail and a turtle by designing training and data sets.

These include input values (e.g. object features) with labels and the desired outcome (e.g. proper classification). For example, supervised learning already helps automate X-ray readings, face recognition, malware detection or weather forecasting.

UL stands for using only input data without a previously defined goal or human supervision. For instance, ML experts use unlabeled dataset, such as animal pictures without the labels ‘’snail’’ and ‘’turtle’’ with the goal that the program makes meaning of the data and detects underlying structures on it own.

The last type of ML is reinforcement learning is based on the reward principle. It all starts with an initial state without any background information, and the program must perform an action, and the system receives negative or positive feedback. The process continues until the desired shape is reached.

4. Deep Learning and Neural Networks

When reading about AI, people often stumble upon two other buzzwords, causing a mild degree of confusion, so let’s also explore what deep learning and neural networks mean. First, to cast some light on the two concepts, I should mention that deep learning is a subfield of machine learning, and the most widely used method for deep learning is by utilising neural networks.

The neural network concept has its foundation in the human biological networks in the brain, which receive signals from nearby cells and decide whether this signal is important and if they should send it further. In the context of machines, the input, e.g. cat’s picture, plays the role of a signal, which is transmitted through different layers and to get to the output (result), which can be if the system decides to categorise the picture as a cat. Applied to more complex scenarios, deep learning using neural networks are behind self-driving cars or voice control systems.

5. What are the Use Cases of AI

I hope you now recognise the differences between AI and ML and that all the attention around these trends is well deserved. Once again, human beings prove that with the right knowledge, using the right tools and techniques, countless inspiring solutions can be developed in the future.

Some of the most promising business domains where AI is already improving core processes are supply chain management, automated quality control, self-driving vehicles, automated support processes (e.g. ticketing systems, chatbots etc.), predictive maintenance. Moreover, AI carries a huge potential to facilitate innovation in the R&D (Research and Development) sector as Big Data and Data Analytics become inseparable parts of obtaining powerful data insights and drive businesses forward.

Aleksandrina is a Content Author at Dreamix, a custom software development company, and is keen on innovative technological solutions with a positive influence on our world. Her teaching background, mixed with her interest in psychology, drives her to share knowledge. She is an avid reader and enthusiastic blogger, always looking for the next inspiration.

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Artificial Intelligence (AI)

How To Get Started With Artificial Intelligence

Artificial Intelligence, among other emerging technologies, is becoming central to the continuous technical revolution, and it is evolving. Therefore it is important to learn about the fundamentals of Artificial Intelligence to understand how it changes and affects different industries.

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Why learning Artificial Intelligence is the best choice

Artificial Intelligence, among other emerging technologies, is becoming central to the continuous technical revolution, and it is evolving. Therefore it is important to learn about the fundamentals of Artificial Intelligence to understand how it changes and affects different industries.

It’s no wonder, then, that the AI industry is full of job openings — so many of them, in reality, that the area now fronts a different challenge: There are too many jobs roles and few fitted applicants. On the upside, that means it provides a virtually assured and well-paying profession for those who have got the right skills.

In this article, we wish to know all about AI, how to get started with Artificial Intelligence, why learning Artificial Intelligence is the best choice, and how to get an ideal job.

1. What is Artificial Intelligence?

Artificial Intelligence strives to imitate and transcend the ability of the human mind using nothing but devices.

The fundamental purposes of Artificial Intelligence are:

  • Expedite up repetitious methods by using humanoids or machines instead of human being
  • Out-think human minds through training and memory
  • Identify patterns and make judgments swiftly and efficiently

Artificial Intelligence strives to develop devices that conceive and reason instead of operating in a comparatively bound space with pre-built systems, methods, and results. Smart Artificial Intelligence devices identify patterns and memorize earlier events and learn from them, making each following judgment more intelligent, rational, and more original.

How To Get Started With Artificial Intelligence

AI is a significant model change in advanced computing and requires a profoundly systematic and logical way to devise machine systems that conceive and learn. In simple words, develop humanoids that are not just robots.

And though it may seem strange, AI abilities are all over the place. A few examples of Artificial Intelligence systems include speech, face and fingerprint identification (available on all smartphones and home devices), email spam blockers, virtual assistant (Siri, Alexa), customer assistance using catboats, Expert systems, game playing, an intelligent humanoid automaton, etc.

3. How to Get Started with Artificial Intelligence

Step-1: Pick a topic you are interested in

Initially, choose a topic that is interesting for you. It will assist you in staying motivated and committed in the training process. Concentrate on a specific problem and seek a solution, rather than just aimlessly reading about everything you can get on the internet.

step-2: Get a prompt solution

The aim is to get any basic solution that includes the query. You require an algorithm that will process information into an acceptable structure for machine learning, prepare a simple model, give a decision, and assess its working.

step-3: Enhance your simple explication

Once you have a simple explanation, it is time for some creative skill. Work on improving all the elements and assess the modifications to conclude whether these changes are worth your effort and time. For example, sometimes, developing preprocessing and data refining gives a tremendous gain on investments than advancing a training model itself.

Step-4: Share your Solution

Publish your solution and share it to get assessed. This will help acquire relevant guidance about your study from other intellects and add up the initial experience in your portfolio.

Follow steps one-four for other queries as well.

Pick other problems and follow the same steps for all tasks. If you have commenced with tabular data form, choose a query that includes operating with pictures or unorganized text. It is also vital to study how to form enigmas for machine learning accurately.

Finish a Kaggle competition

This competition enables you to examine your abilities, working on the same problems several other engineers work on. You will be asked to undertake various ways, picking the most efficient explications. This competition can also prepare you for collaboration as you communicate with people on the panel, exchanging your thoughts and learning from others.

Apply machine learning professionally

You have to decide what your career aims are and build your portfolio. If you are not yet ready to apply for machine learning jobs, seek more projects that will make your portfolio effective.

Artificial Intelligence (AI)

4. Why learning Artificial Intelligence is the best choice

The understanding of Artificial Intelligence initiates lots of new openings. It is sufficient to comprehend the basics of this technology to learn how machine tools work. As you study more about AI, you get an opportunity to grow as a developer who will build high-level AI applications. There are infinite opportunities in this domain.

For those who desire to start learning Artificial Intelligence, It has many options available. The internet enables everyone to enroll in online classes. Some of the courses focus on students who already have a specific level of technological expertise and converge on coding. In contrast, other Artificial Intelligence and machine learning online paths will help even those who do not have knowledge and beginners in the programming and engineering field.

5. How to take a job in Artificial Intelligence?

Humanoids may be arriving for some tasks, but they will create new job roles as well. Here are some ways to enhance your application, whether you are operating in the domain required to fill.

1. Check out the best online courses for ai and machine learning

Similar to many tech-based areas, there are numerous online courses for ai and machine learning topics, letting someone learn more about the area as a whole or gain more specialized knowledge. Some options offer ai certification online that can reinforce a resume.

2. Join outside organizations

Acquiring from others in the field can assist you in enhancing your abilities, so check out social meetups. Getting associated with organizations that allow data scientists to work on the new data, exercising and improving the skills while studying from their companions.

3. Add Quality industry experience.

Many of the in-demand AI jobs positions are technical by nature, but understanding how to interpret those advancements to other businesses or customers is crucial for any business.

4. Put a habit of reading a lot.

Those working in the Artificial Intelligence field should always be studying, and reading is a way to do that. Read a lot. Not just about your domain but all the related sectors.

5. Be prepared to grasp

AI is a swiftly advancing field, so be ready to examine as many experiences and opportunities in AI as feasible.

Keeping up to date on the most advanced research in AI is very important. There is so much progress befalling each day. This may involve seeking opportunities inside the current working place or outside. Make sure that you do not get restricted into one area.

Apart from having the standard skill-set and education, these are additional methods to be a powerful applicant for these profitable jobs.

Conclusion

Since its beginning, Artificial Intelligence has been performing a requisite role in the technology sector, advancing the quality of living across multiple enterprises. And speaking about what tomorrow endures, it is difficult to foretell. However, the way AI is developing, It appears the innovations in the following times will be fabulous.

These inventions will only be prosperous when there are well-qualified people and serve in Artificial Intelligence. Suppose you have a desire to work with this wondrous technology. In that case, it is time you check out the online classes for Artificial Intelligence which can assist you in growing an expert in the Artificial Intelligence field.

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