Major impacts of artificial intelligence

While we’re probably heading toward a future where machines might become intellectually equal or superior to humans, let’s take a look at how artificial intelligence is transforming the present world now.

• Highly personalized production: Advancements in artificial intelligence together with software intelligence are empowering businesses to offer products and services that are extremely relevant to individual customers. According to studies, brands that are willing to personalize products are able to develop greater trust with their consumers. Studies also revealed that a huge percentage of customers are willing to have trusted brands use their personal data to receive targeted and tailored products, offers, and recommendations.
• No more repetitive tasks: Repetitive tasks aren’t only monotonous but consume a huge amount of time and energy of the human workforce as well. With the help of artificial intelligence, many of these tasks are being carried out by machines as they can think faster than humans and are capable of multi-tasking. Additionally, as their time and speed are calculation based parameters, those can be adjusted, if required.
• Almost flawless medical diagnosis: Artificial intelligence has enabled the healthcare industry to effectively analyze possible advert scenarios by advancing the process of finding solutions or preventing. It has also made healthcare accessible for those who’ve had unequal access until today, because now it has become possible to build a personalized way for inclusion for each one.
• Personalized education: It’s a fact that it’s impossible for a single educator to work with each student at once. With the emergence of a personal AI assistant or tutor, today it has become possible for students to get one-on-one, additional help in areas of required development. While some educators may fear the idea of artificial intelligence replacing them in the future, the reality is, human intelligence and AI working in tandem will only drive us forward.

Parting Thoughts

Virtually, there’s nothing that artificial intelligence won’t be able to support but it requires human expertise to oversee, assign its responsibilities, and identify its limitations. When the best parts of AI would be coupled with the best parts of humanity, it’ll take us to a different level together than either one could do individually. So, it can be safe to say that it’s probably the best time to learn artificial intelligence.  A comprehensive AI training would not only help you get an edge in today’s competitive job market but will make you future-proof as well.

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To learn more about artificial intelligence, click here and read our another article.

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1- E-learning

Artificial Intelligence is changing e-learning drastically. You can use AI to personalize learning for every individual student. Rather than having the same study and training materials for each student, you can use AI-enabled hyper-personalization to create a custom learning profile of every student and then customize the study materials based on their preferred style of learning, ability, and experience. This would be a boon to educators as they can take advantage of augmented intelligence assistance that offers them an extensive variety of materials leveraging the same core curriculum and yet, let them meet the specific needs of every student. Apart from helping deliver smart content and personalized learning, you can also use artificial intelligence to automate administrative tasks like evaluating homework, grading exams, offering valuable responses to students, etc.

2- E-commerce platforms

Artificial Intelligence can help in predicting customers’ likes and preferences based on their past shopping behavior. Amazon’s artificial intelligence has been doing it for a long time and the e-commerce giant has given its sales a massive boost with AI’s predictions and suggestions. What’s impressive is that more than a third of the company’s sales are attributed to its recommendations. With the passage of time, Amazon’s algorithms have become more and more sophisticated, precise, and useful. In case you decide to join an e-commerce platform after your AI training, you can use artificial intelligence to find out your customers’ preferences and shopping behaviors with the most incredible precision.

Though Amazon hasn’t been there yet, the company is planning to put into practice a shipping system using AI that delivers products even before you put a request for them or know you require them.

3- OTT platforms

OTT (over-the-top) platforms have changed the way we consume audio or video. Over the past few years, a significant consumer shift from traditional audio/video content to home entertainment via Internet-connected devices has been observed. With the trend predicted to grow in the coming years, the war has heated up among streaming service providers like Netflix, Amazon, etc. As it’s a big challenge to acquire and retain OTT customers, especially in a highly competitive market, the leading players are using artificial intelligence to build a robust recommendation engine and content discovery mechanism that helps to deliver consistent user experience. After you learn artificial intelligence and complete your AI training, you too can take a plunge into the world of OTT platforms to experience first-hand how it could help decide the fate of a platform by helping to offer content and an experience that’s a notch above the rest.

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Here’re the major ways through which the finance industry is leveraging the power of artificial intelligence and machine learning.

1- Better risk management

Probably the biggest impact of artificial intelligence and machine learning on the finance industry can be found when it comes to risk management. While traditional software applications can predict creditworthiness based on the static information obtained from financial reports and loan applications, implementation of machine learning technologies can help financial institutions to go much further. Algorithms identify the signs of probable future issues and analyze a client’s history of risk cases to help the authorities make an informed decision. They’re also able to identify present market trends together with relevant news items which can affect the ability of a client to pay.

2- Improved data security

Data security has always been at the top of the list of concerns for any financial institution. And if you consider the number of data breaches occurred during recent years, there’re reasons to be concerned. Traditional security tools aren’t capable of identifying modern sophisticated cyberattacks. To mitigate security risks, financial institutions implement advanced technologies like machine learning. Security solutions powered by machine learning are come with unique abilities to secure the financial data. The combined power of big data capabilities and intelligent pattern analysis gives machine learning security technology a robust advantage over traditional tools.

3- Enhanced customer experience

Like all other industries, the financial industry is also focusing on developing the top line by implementing advanced methods to offer custom services and better experience to customers. Many financial institutions have already introduced chatbots powered by artificial intelligence abilities that can analyze the voice of a customer and converse accordingly. With the help of machine learning and big data, these chatbots understand how to respond to the questions of customers’ – from transaction-specific questions to onboarding concerns. Additionally, technologies backed by artificial intelligence and machine learning are capable of making product recommendations and handling customer requests.

Parting Thoughts

In the finance industry, the disruption triggered by artificial intelligence and machine learning is increasing exponentially and toward greater economic impact than ever, both on the customers and the industry. By addressing all the major operational aspects and adding advanced features, these technologies are not only revolutionizing the entire industry but also improving the financial health millions of customers involved in the process. And from a business perspective, these technologies are driving a more fundamental and deeper shift in the finance industry.

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1- Start with the basics

The first thing you should focus on is to learn a programming language. While there’re lots of languages that you can begin with, Python is preferred by many aspiring artificial intelligence professionals because it comes with libraries which are better suited to ML (machine learning – one of the happening subsets of artificial intelligence). There’re some good ways to learn Python – from self-learning to attending a program. If you don’t have any idea about programming languages, you should go with the latter option.

2- Leverage the power of videos and podcasts

Once you’ve obtained some programming language knowledge, the next step is listening to useful videos and podcasts related to artificial intelligence. They’ll help you gain more comprehension about the present trends and happenings in the industry, emerging technologies and how they’re being implemented in the field, their effects in the real life, and many more. Remember to get some amount of familiarity with the concepts and jargons involved as these videos and podcast often come with them mentioned.

3- Attend an artificial intelligence course

This is probably the best and most effective way to learn artificial intelligence. A dedicated course on the subject will greatly help you in learning about the world of artificial intelligence. It’ll help you get immensely valuable exposure to the required skills. Usually, this type of courses brush up on the fundamentals you’ve obtained already and then help you develop the technical skills needed to work with artificial intelligence in today’s professional world.

4- Keep on reading articles and books on artificial intelligence

While attending a guided course on artificial intelligence will prepare you to enter the professional world, lots of amazing articles and books are also available which would help you strengthen your theoretical knowledge.

5- Keep on practicing

Like any other field, proper practice is the best way to learn artificial intelligence. So, it’s extremely important to look for projects and obtain practical knowledge while doing them. Apart from the projects you’ll be in an artificial intelligence course, you should constantly work on other related projects not only to build your portfolio but to strengthen your knowledge about the field as well.

Final takeaway

Artificial intelligence is one of the most promising technologies we’ve these days and billions of dollars are being invested in startups or artificial intelligence projects. The technology has the ability to transform almost every industry to a great extent. So, start your process of learning it as soon as possible to get prepared to join the artificial intelligence revolution.

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• Artificial intelligence is the intelligence demonstrated by machines. Any machine that understands its environment and is able to take actions that increase its chances of achieving some goals, can be described as an artificial intelligence-enabled machine. On the other hand, machine learning is one of the present applications of artificial intelligence.
• When machine learning goes beyond simple programming and can mirror and interact with people, even on the fundamental level, artificial intelligence comes into the picture. Though artificial intelligence needs machine learning to optimize decision, the former is the step beyond the latter. Artificial intelligence utilizes what it has obtained from machine learning to simulate intelligence.
• In artificial intelligence, a machine learns by gathering knowledge and understanding how to apply it. Here, the goal is to increase the chances of finding an optimal solution. It’s the study of training computers to try to do things which a human can do better at present. On the contrary, in machine learning, algorithms obtain the skill or knowledge via experience. It depends on big datasets to keep on reminding the data to identify common patterns.
• Based on capabilities, artificial intelligence can be distributed into two categories namely general AI and narrow AI. Based on learning methods, Machine learning can be distributed into three categories namely supervised learning, unsupervised learning, and reinforcement learning.
• The objective of artificial intelligence to develop smart systems like humans that can solve complex problems. The goal of machine learning is to let a machine learn from massive datasets so that they can provide accurate output.
• The key applications of artificial intelligence include customer support using chatbots, Siri, intelligent humanoid robot etc. The key applications of machine learning include Google search algorithms, online recommender system, Facebook friend tagging suggestions etc.
• Artificial intelligence holds a broad range of scope while machine learning comes with a limited scope.
• Artificial intelligence deals with structured, unstructured, and semi-structured data while machine learning works with only structured, and semi-structured data.

Both machine learning and artificial intelligence can leave valuable business implications. In the context of the coming future, both are imperative to our society. A robust understanding of both of these fields will be extremely important to comprehend the rapidly changing business world and how the devices we use everyday work. The promises and value of both these fields are being materialized because of each other. If you’re an aspiring candidate looking to step into these fields, this is probably the best time to begin your journey. As advancements and adoptions of both artificial intelligence and machine learning continue to accelerate, one thing can be assumed for sure – the impact will be profound.

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But before delving deeper, let’s have a quick look at what are AI and machine learning basically.

1- AI and machine learning – What they are

Fundamentally, AI or artificial intelligence refers to the intelligence demonstrated by the machines. And ML or machine learning is a way using which professionals achieve AI. Machine learning can be considered as the ability of machines to learn utilizing statistical techniques without being programmed explicitly.

2- Ways AI and machine learning are facilitating average people’s lives

The concepts of AI and machine learning aren’t completely foreign to us as they have been heavily explored by popular media. There are lots of movies which have shown us a world where AI-enabled robots and machines hold the dominating power. And these have triggered, to a good extent, lots of negative impressions about AI and machine learning among the average people. However, despite how negatively movies demonstrate the power of AI and machine learning, these technologies are truly transforming average human lives into better ones. Let’s explore the most common aspects that are being impacted by AI and machine learning.

2.1- Banking and financial services

It’s hardly possible to count the number of people that have bank accounts. In addition, just consider the number of their associated facilities like credit cards which are in circulation. Now imagine how many hours human employees of these institutions would have to invest to sift through the transactions that are performed every day? And how much time and effort it would take to identify an anomaly? With the help of AI and machine learning, a huge number of banks and financial institutions have become able to review the quality of various applications and to analyze and predict risks, in an effort to make informed decisions. The so-called traditional industry is implementing AI and machine learning to increase user engagement. High-end technologies like predictive analysis, chatbots, voice recognition etc are helping minimize the gap between potential customers and financial institutions. These days, it’s possible for any customer to contact any of these establishments anytime and from anywhere and receive real-time replies.

2.2- Healthcare services

Both AI and machine learning have already acquired a significant part in our well-being and health. From being utilized for faster patient diagnosis to the prevention of illnesses – these technologies are being used on a regular basis by lots of healthcare service providers. These days, it’s possible to predict the potential health hazards a person may be susceptible to, depending on his/her genetic history, socio-economic status, age etc – which was simply unimaginable before the emergence of AI and machine learning. With the help of AI and machine learning-powered programs, healthcare service providers can cross-reference symptoms against databases that contain millions of cases of illnesses to expedite the process of diagnosing disease and illness, saving lives through faster and appropriate treatment. These technologies are also being adapted to expedite research works toward cures of different diseases.

2.3- Email

Almost every person uses email these days for a huge number of purposes. It may sound unlikely but your email inbox is a place where advanced technologies take place on a regular basis. There are two key aspects where email service providers use AI and machine learning. First comes the advanced spam filter. Unlike plain rule-based filters that aren’t much effective against spam as spammers can update their messages quickly to work around them, advanced spam filters continually learn from a wide range of signals like message metadata, words in the message etc to prevent spam. Another aspect is smart email categorization. You’ve probably seen that Gmail uses an approach to categorize the emails into primary, promotion, social inboxes. This is made possible with the help of AI and machine learning together with manual intervention from users. When some messages are marked in a constant direction by a user, a real-time increment to that threshold is performed by Google in order to achieve appropriate categorization.

2.4- Transportation industry

There’s a heavy influence of AI and machine learning on the present transportation industry can be found. These technologies have been instrumental in lowering threats triggered by reckless driving via the deployment of automation and sensory management. There are vehicles that can understand their surrounding parameters and thus, can take precautionary measures whenever needed to ensure passenger safety. Apart from vehicles, AI and machine learning technologies are to be deployed soon to prevent traffic congestion on roads and for traffic management.

2.5- Taking over tedious and hazardous jobs

AI and machine learning can seem to be a boon to humanity when we consider the fact that they liberate humans and enable them to focus on tasks in which they excel. These technologies take care of a wide range of tedious tasks that have to be performed in order to attain different results. Machines excel in performing cumbersome tasks, leaving enough time and room for humans to focus on more creative aspects of a business. In the financial sector, for example, AI and machine learning help financial analysts to get some relief from the monotonous nature of their jobs and concentrate on deeper analysis and research of all-round customer experience. In the context of hazardous jobs like bomb disposal, welding etc, AI and machine learning are helping the professionals to a great extent. These days, machines are taking over those jobs with the help of human intervention.

2.6- Social networking

Almost everyone has experienced it several times. When a user uploads pictures to Facebook, the faces get highlighted automatically and the service suggests friends to tag. If you wonder how it can find out which of your friends are in the picture, Facebook uses AI and machine learning techniques to recognize faces. It also uses these technologies to personalize their users’ newsfeed and ensure that they are viewing posts that interest them. Apart from Facebook, almost all other social networking platforms including Pinterest, Instagram, Snapchat etc leverage AI and machine learning to maximize user experience.

2.7- Online shopping

Online shopping has become almost an inevitable part of life for today’s tech-savvy customers. Have you ever wondered how e-commerce websites quickly return with a collection of the most relevant items related to your search? AI and machine learning are technologies that make it possible. Personalized recommendations on their home page, product pages etc are also examples of their deployment. Fraud protection is another aspect where these technologies perform a great job. Here, AI and machine learning are deployed to not only avert fraudulent transactions but to lower the number of legitimate transactions that are declined because of being falsely marked as fraudulent.

2.8- Home security and home automation

When it comes to home security, these days, a significant number of homeowners are deploying cutting-edge systems are deploying high-end cameras and security systems powered by AI and machine learning. These systems are capable of building a catalog of the frequent visitors of a home and thus, can detect uninvited guests instantly. Smart homes also offer a multitude of different types of useful features such as providing notification when the kids come back from school etc. When combined with appliances, AI and machine learning can make household management and housework seamless. From allowing the refrigerator to communicate with the oven to replenishment of food and supplies – all have become possible.

Final Thoughts

From the above examples, it can be concluded that a significant number of things, which were simply unimaginable before the emergence of AI and machine learning, have become possible these days. However, similar to other technologies, AI and machine learning also come with a significant number of negative concerns. The biggest one of them is that these technologies will replace humans in performing several tasks, making people jobless eventually. However, if these technologies are looked upon as tools rather than replacements, businesses should be able to attain a huge industrial growth. According to many experts, AI and machine learning have an opportunity to work together with humans. By nature, humans are good at raising the right questions while AI and machine learning are good at dealing with huge amounts of information. By working together they can leave a huge business impact. The future of these technologies isn’t exactly clear today, but they’ll surely have an impact on society as they are doing right now. We’ll have to wait to see whether that impact turns out to be positive or negative but it can be said that these technologies have a huge potential to make the lives of the people easier to a great extent.

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Before taking a deeper look into future jobs for data science professionals, let’s take a closer look into how artificial intelligence and machine learning have evolved over the years and what lies ahead in store for these domains.

Artificial Intelligence – its origin and evolution

It was mathematician and scientist Alan Turing who spent a lot of time after the Second World War on devising the Turing Test. Though it was basic, this test involved evaluating if it was possible for artificial intelligence to hold a realistic conversation with a person, thus convincing them that they were also human.

Since Turing’s Test, AI was restricted to fundamental computer models. It was in 1955 when John McCarthy – a professor at MIT, coined the term “artificial intelligence”. During his tenure at MIT, he built an AI laboratory.  Full List Processing (LISP) was developed by him there. LISP was a computer programming language for robotics intended to provide expansion potential as technology improvements happened with time.

Though promise was shown by some base model machines – be it Shakey the Robot (1966) or Waseda University’s anthropomorphic androids WABOT-1 (1973) and WABOT-2 (1984), it wasn’t until 1990 when Rodney Brooks revitalized the concept of computer intelligence. But it took many more years for artificial intelligence to evolve as it was only in 2014 that Eugene, which was a chatbot program designed by the Russians, was able to successfully convince 33% of human judges. According to Turing’s original test, more than 30% was a pass, though plenty of room was left for stepping it up in the future.

From its humble beginnings, artificial intelligence (AI) has evolved as perhaps the most significant technological advancement in recent decades across all industries. Be it the robotics aspect of AI, or the implementation of machine learning (ML) technologies that are driving useful insights from big data, the future seems to hold a lot of promise. In fact, the enhanced information extracted from large chunks of data is helping companies today to mitigate supply chain risks, improve customer retention rates, and do a lot more.

An example of how these technologies could change the way we live and work became evident when Amazon introduced its Alexa in the workplace. However, many believe that the AI-powered, voice-activated device signals just the beginning. Thanks to NLP (natural language processing), which is made possible via machine learning, modern computers, hi-tech systems, and solutions can now know the context and meaning of sentences in a much better way. As NLP becomes more improved and refined, humans will start communicating with machines seamlessly exclusively via voice without the need of writing code for a command. Thus, professionals who can design and test devices based on NLP and voice-driven interactions are likely to be in high demand in the future.

With the growing interest and implementation of artificial intelligence in various fields and the promising future the global machine learning market (predicted to grow to $8.8B by 2022 from $1.4B in 2017, according to a report by Research and Markets), there’s bound to be a wide variety in future jobs for data science professionals as well those specializing in AI and ML.

Future jobs to consider in the field of data science with an emphasis on AI and ML

Data scientists would continue to be in demand though a new position of machine learning engineer is giving it a tough competition as more and more companies are adopting artificial intelligence technologies.  In many places where data specialists are working, this relatively new role is emerging slowly. Perhaps you are now wondering who a machine learning engineer is, what the skill requirements for this position are and what kind of salary is on offer.

Let’s try to find answers to these questions.

Who’s a Machine Learning Engineer?

These are sophisticated programmers whose work is to develop systems and machines that can learn and implement knowledge without particular direction.

For a machine learning engineer, artificial intelligence acts as the goal. Though these professionals are computer programmers, their focus goes further than particularly programming machines to execute specific tasks. Their emphasis is on building programs that will facilitate machines to take actions without being explicitly directed to carry out those tasks.

The roles performed by these professionals include:

• Designing machine learning systems
• Studying and changing data science prototypes
• Choosing
• Doing research and applying suitable ML tools and algorithms
• Selecting fitting datasets and methods for data representation
• Developing machine learning applications that are consistent with the requirements
• Conducting machine learning experiments and tests
• Carrying out statistical analysis and modifying them using test results
• Training and retraining systems, as and when needed
• Broadening the existing machine learning frameworks and libraries
• Being well-informed on the developments in the domain

When it comes to the skill sets that machine learning engineers need, there are some that are common with those required by data scientists such as:

• Programming Languages: Though Python is considered the leading language, you’ll probably have to learn R, C++, and Java. At some point, you’re likely to work on MapReduce as well.
• Statistics: You’ll need to be familiar with vectors, matrices, matrix multiplication, etc.
• Data Cleaning and Visualization: With data cleansing, you’ll help to boost the efficiency of companies and even save their precious time. Data visualization would be equally important as it could have a make-or-break effect when it’s about the impact of your data.
• Machine Learning techniques: You’ll need to have a good understanding of machine learning techniques such as decision trees, supervised machine learning, logistic regression, etc. Knowledge of deep neural network architectures is equally important as believed to be the next level in the domain.
• Big Data Processing Frameworks: As a humungous amount of data gets generated today at a great speed, you need frameworks such as Spark and Hadoop to handle Big Data. Since a majority of organizations these days are using Big Data analytics to get hidden business insights, you need to be confident in your use of these Big Data processing frameworks as a machine learning
• Industry Knowledge: Even when you have all the technical skills mentioned above, you won’t be able to channel them productively if you lack business acumen and don’t know what elements contribute toward a successful business model. Be it helping the organization discover new business opportunities, or being able to distinguish the potential challenges as well as existing problems that need to be solved for the business to keep going and growing, you should know how the industry functions and what will benefit the business.
• Computer Vision (CV): ML and CV are Computer Science’s two core branches that can function and power extremely sophisticated systems that depend exclusively on ML and CV algorithms. However, by combining the two, you can do much more, which makes it important to have a solid understanding of both ML and CV.

In addition to the above, you must have the following ML engineer skills:

• Language, Video, and Audio Processing: This includes having good control over libraries like NLTK, Gensim, and techniques like sentimental analysis, word2vec, and summarization.
• Signal Processing Techniques: You should have a robust understanding of these techniques, which would be needed to solve different problems.

Additionally, you should have knowledge of applied Mathematics (with emphasis on Algorithm theory, quadratic programming, gradient descent, partial differentiation, convex optimizations, etc.), software development (Software Development Life Cycle or SDLC, design patterns, modularity, etc.), and time-frequency analysis as well as advanced signal processing algorithms (like Curvelets, Wavelets, Bandlets, and Shearlets).

Other jobs

Apart from data scientist and machine learning engineer, some other future jobs for data science professionals could be

• Data Architect/Data Engineer
• Data Analyst
• Big Data Engineer
• Artificial intelligence Experts
• Information Security Analysts
• Process Automation Experts
• Robotics Engineers
• Human-Machine Interaction and User Experience Designers
• Blockchain Specialists

Thus, with the increasing adoption of artificial intelligence and machine learning, there won’t be any dearth of future jobs for data science professionals.

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To learn more about data science, click here and read our another article.

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1- What is deep learning?

In its simplest form, deep learning, also known as deep machine learning or deep structured learning, is a subset of machine learning and refers to neural networks that have the ability to learn the input data’s increasingly abstract representations. These days, implementation of deep learning techniques can be found to a great extent, from self-driving cars to academic researches.

2- What sets deep learning apart?

If you follow prominent job portals, you can find that there’s a significant number of deep learning professionals job positions almost all of which are paying really well. Now, you may wonder why do companies hire these professionals? Or, what can such a professional bring to them? Let’s have a look.

2.1- Quality and accuracy

Every company wants quality and sometimes work produced by human employees come inferior and with errors. This is particularly true for data processing repetitive tasks. However, a worker powered by deep learning is capable of developing new understandings and producing high-quality, accurate results.

With the help of deep learning, software robots can understand spoken language, recognize more images and data, and work more efficiently. These are the main reasons why companies across the globe are hiring deep learning professionals.

2.2- Increased cost and time benefit

In its simple form, neural networks can be considered as trainable brains. These networks are provided with information and trained to do tasks, and they’ll use that training together with new information and their own work experience when it comes to accomplishing those tasks.

Implementation of deep learning in business can save the company a significant amount of time and money. In addition, when time-consuming or repetitive tasks are done efficiently and quickly, employees are freed up to take care of creative tasks that actually need human involvement.

3- Deep learning vs. Machine learning

As deep learning is a branch of machine learning, general people often become confused about when to use over the other. In general, when it comes to large datasets, deep learning should be the ideal technique while traditional machine learning models can do perfectly well with small datasets.

Deep learning outperforms traditional machine learning in the context of complex problems like speech recognition, natural language processing, image classification etc. Another key difference between them is that deep learning algorithm needs a long time to be trained because a large number of parameters while traditional machine learning algorithms can be trained within a few hours. Interpretability is another reason for which many companies prefer using machine learning over deep learning.

4- Guide to deep learning structure

Deep learning is a complex field consisting of several components. In this deep learning structure guide part of the post, we’ve put together the major elements that you’d need to master upon.

Also, we’ve designed this deep learning guide assuming you’ve a good understanding of basic programming and basic knowledge of probability, linear algebra and calculus. Let’s have a look at the guide.

4.1- Fundamental of machine learning

It’s imperative to get a good understanding of the basics of machine learning before you dive into deep learning. Basically, it’s distributed in three types of learning – supervised, unsupervised and reinforced learning.

In deep learning, a significant amount of machine learning techniques like logistic regression, linear regression etc are used. There’re lots of resources available that can help you accomplish this goal. You should also learn Python at this stage. Try to get yourself introduced to scikit-learn, a widely used machine learning library. At the end of this stage, you should have a good theoretical as well as a practical grasp of machine learning.

4.2- Introduction to deep learning

The first thing you should do is understand the frameworks of deep learning. Deep learning professionals mainly need to work with algorithms which are inspired by neural networks. Though there’re lots of resources available online that you can use to learn the basics of deep learning, it’s recommended to take a course from a reputed institute.

Try to get access to a GPU (graphics processing unit) to run your deep learning experiments. If possible, try to read some research papers in deep learning as they cover the fundamentals. At this stage, try to pick any of the three – PyTorch, TensorFlow or Keras. Whatever you choose, be sure to become very comfortable with it.

4.3- Introduction to neural networks

A neural network comes with a layered design that contains an input layer, a hidden layer, and an output layer. It functions like the human brain’s neurons such as receiving inputs and generating an output.

There’re several types of artificial neural networks that are implemented based on a set of parameters needed to determine the output and mathematical operations. The functions of these neural networks are utilized in deep learning which helps in image recognition, speech recognition, among others.

4.4- Fundamentals of Convolutional Neural Networks

Put simply, Convolutional Neural Networks are multi-layer neural networks which consider the input data as images. It’s widely used in facial recognition, object detection, image recognition and classification etc. The best thing about Convolutional Neural Networks is the need for feature extraction is eliminated. The system learns to perform feature extraction.

The fundamental concept of CNN is, it utilizes convolution of images and filters to produce invariant features that are passed on to the next layer. In the next layer, the features are convoluted with a different set of filters to produce abstract and more invariant features and this process continues till we get final output/feature that is invariant to occlusions.

4.5- Understanding unsupervised deep learning

Unsupervised learning is a complex method with the goal of creating general systems which can be trained using a very minimum amount of data. It comes with the potential to unlock unsolvable problems which were done previously. This method is widely used to solve the problems created by supervised learning.

4.6- Introduction to natural language processing

Natural language processing is focused on making computers capable of understanding and processing human languages in order to get them closer to the human-level understanding of language. This domain mainly deals with developing computational algorithms that can automatically analyze and represent human language. It can also be used for dialogue generation, machine translation etc.

4.7- Introduction to deep reinforcement learning

Through this technique, software or a machine can learn to function in an environment by itself. Though some may compare reinforcement learning with other forms of learning like supervised and unsupervised learning, there remains a major difference. It’s that reinforcement learning isn’t provided with outcome instructions, instead it follows trial and error mechanism to develop appropriate outcomes.

5- Major applications of deep learning

Here’re some real-life applications where deep learning is used heavily.

5.1- Speech recognition

You’ve probably heard about Apple’s intelligent assistant Siri, which is controlled by voice. The tech giant has started working on deep learning to develop its services even more.

5.2- Instant visual translation

You’re probably aware of that deep learning is utilized to identify images which contain letters and once they’re identified, those can be turned into text and translated, and the image can be recreated using that translated text. In general, this is called instant visual translation.

5.3- Automatic machine translation

You may have already heard about the translation ability of Google. But did you know what’s the technology behind Google Translate? It’s machine translation that tremendously helps people who cannot communicate between themselves because of the difference in language. You may ask that this feature has been around for some time now, so there shouldn’t be anything new in this. Using deep learning, the tech giant has completely reformed the machine translation approach in Google Translate.

Here, we’ve only mentioned some popular real-life cases that use deep learning extensively and showing promising results. There’re lots of other applications where deep learning is successfully being implemented and demonstrating good results.

Final thoughts

So, this is the overview of deep learning in a simple form. Hopefully, by now you’ve got a clear idea of what should be a good deep learning structure to follow in order to become a deep learning professional.

With the entire business landscape steadily leaning toward artificial intelligence together with massive amounts of data being generated every single day, the future surely holds a great place for deep learning professionals. The key reason behind this is the supremacy of deep learning in terms of accuracy when properly trained with an adequate amount of data. If you’re interested to step into the field, probably this is the best time to start your journey because the big data era is expected to provide massive amounts of opportunities for advancement and new innovations in the field of deep learning.

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To learn more about data science, click here and read our another article.

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However, there’re still some technical issues with virtual reality related to optimization and rendering. Until now, all advancements in the field were focused mainly on better hardware and uninterrupted and increased frame-rate. However, recently an idea of using AI for virtual reality has emerged, which will bring a multitude of benefits. The main reason is that big data and AI are perfectly suited for pattern recognition and hence, similar pattern generation. This method of working can generate a new bunch of advantages.

Over the next few years, virtual reality applications will likely to become increasingly sophisticated with the emergence of more powerful devices that are capable of developing higher quality visuals. The understanding of how we can usefully interact and navigate within virtual environments will also evolve, resulting in the development of more natural methods of exploring and interacting with virtual space. Here’re some near-coming effects of immersive virtual reality experiences boosted by the power of AI.

1- Hazard warnings

Apart from their own ability to judge a situation within a fraction of a second, humans have developed a diverse range of mechanisms that can help them stay safe from danger. However, these judgments that are usually called intuitions aren’t infallible.

What if a machine that has a combined experience of thousands of people could overtake such a task? Such a development can save millions of soldiers on the battlefield by helping them in anticipating the moves of opponents and alerting them in advance. AI has already been employed in different military strategies. But with this implementation, battlegrounds of the future will become a more high-tech environment.

2- Customized simulators

AI combined with virtual reality/augmented reality is a strong combination that can be used as a tool for educating the next generation of pilots, surgeons, among others. Today, with the help of virtual reality, we can learn to drive a car safely, without endangering our or the instructor’s life. In addition, for some activities, this also proves to be an effective way of reducing costs, as some real-life activities involve expensive supplies.

AI can replace numerous situations that occur randomly and learn from the student’s behavior. As the student gets better, the system will present increasing difficult situations. AI has the ability to improve simulated training by incorporating more data points, comparing as well as contrasting different techniques, and by personalizing the education. The improved system will act more like a customizable trainer instead of a static simulator. With a simple headset and a set of sensors, we should be in a position to learn everything. Virtually anyone should be able to get access to world-class coaching at any sporting or academic discipline.

3- Physical environment mapping

Today some furniture providers offer apps that provide the users with the ability to try out furniture, after carefully inputting the size and obstacles such as doors and windows of their rooms. What if the process becomes faster and more accurate by just scanning the room with a user’s phone?

AI has the ability to help map environments in real-time and merge those results with a virtual world. The result is that users get a fully immersive virtual reality experience with real-world structures. The fledgling system comes with the ability to generate CAD-quality models of a house so that users can try decorations and furniture before they buy. With a bit more training, the system can offer on-demand design services. For instance, the users select a style and the necessary things, and the system comes up with a complete plan, much like what an interior designer does.

4- Game development

As a primary application of immersive technologies, it’s safe to assume that gaming will continue to be one of the major driving forces for virtual reality’s progression, and in this endeavor, AI can help to a great extent. First, it’ll replace the present method of animation. Right now, two methods are applied for animating characters – manual CG work and motion capture.

Motion capture is restricted to the physical capabilities of the actor while handcrafted animations are highly laborious. Motion capture involves recording a huge array of movements which are essentially repeated time and time again. New systems utilize machine learning to merge a huge library of the stored movements and then map them onto characters that are being developed. This’ll open up a new domain of realistic animation in the context of cartoons, video games, and virtual reality environments. Even non-player characters may become part of the story in a more believable and relatable way.

5- Immersive traveling experience

Virtual reality isn’t only about beautiful worlds where people can lose themselves. It can also come up with an amazing replica of locations in the real world that are costly or somewhat impossible to reach for the common people.

Development of immersive travel experiences can be as close as it gets to the actual thing for some demographics. It can also become a new type of entertainment for people who’re passionate about traveling.

6- True socializing

Facebook’s heavy invest in virtual reality with its acquisition of Oculus Rift, we’ve already received a hint about that one day, social media will likely to get a boost from the virtual reality immersive experiences powered by AI.

In the future, AI may have the task of designing users’ social media avatar by considering both their pictures and preferences. In the near future, we may be in a position to meet our friends in virtual environments. The concept requires mind-boggling processing power, but AI together with virtual reality has the ability to make it possible.

7- Rendering optimization

One of the major challenges in virtual reality/augmented reality is delivering realistic graphics with present day’s consumer hardware. A huge amount of complexity results into lag and pixelated images that in turn results into problems for virtual reality headset wearers. As a result, most of the virtual reality experiences available today are lacking in convincing detail and simplistic.

However, in virtual reality, AI techniques can be used for selective rendering where only some specific portions of a scene are dynamically generated. AI techniques can also help to compress images intelligently, enabling faster transmission over wireless connections without any understandable loss in quality.

8- Challenges with more immersive content

Implementation of AI for virtual reality/augmented reality is expected to offer more immersive technology which will be increasingly personalized. The drive to capture people’s attention generates two challenges. First, a lack of authority over personal data may drive the users away from the long-term adoption of the new technologies. User privacy and data controls have become key concerns for customers. Given the improved data tracking features of immersive technologies, from tracking facial expressions to eye-movements, the personal data will become at more risk, making privacy a more serious concern. Secondly, the well-being of the users will become at stake. Let’s have a look at some probable steps that can be taken to mitigate these challenges.

8.1- More authority to users about their personal data

It’s a fact that major virtual reality companies use cookies to store data, while collecting information on the browser and device type, location, among others. In addition, communication with other users in virtual reality environments is being stored and sometimes shared with third parties for marketing purposes. It leads to the necessity of a solution that acts like a buffer between companies and users.

8.2- Regulatory frameworks

The privacy concerns associated with traditional media has already started arising in immersive content. If developers aren’t willing to provide agreeable and clear terms of use, regulators need to step in to protect the consumers, as already done by some jurisdictions.

As companies develop advanced applications using immersive technologies, they should focus on the transition from using metrics that only measure the amount of user engagement. Alternative metrics may include something like a net promoter score for the software that would indicate how strongly consumers recommend those services to their friends based on their own experience with them.

Final thoughts

Lagging hardware and costly barriers have caused virtual reality to become overhyped over the last few years. With the implementation of AI, organizations can overcome earlier technical barriers while improving realism. These are only some of the possible applications of AI in virtual reality.

As the technology becomes more widely accepted, we can expect to see more innovative applications in the near future. However, more work on the part of the developers will be required if immersive technologies are to generate more interactions with the content and media.

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To learn more about artificial intelligence, click here and read our another article.

The post Immersive Virtual Reality AI and Its Near-Coming Effects appeared first on Magnimind Academy.

For example, the internet that has entirely transformed the way we interact, socialize, work, and share information with each other. According to many, the emergence of blockchain is probably the biggest tech develop after the internet that has the ability to disrupt technology.

1- What’s blockchain technology?

Put simply, it’s a distributed ledger where transactions between two parties are recorded efficiently and in a permanent and verifiable manner. You can consider it as a developing list of blocks that can also save transactions which haven’t entered any previous block. Blocks can be considered as a lasting store of records that, once encrypted, cannot be changed or removed. In this technology, information is distributed without letting others copy it.

All blocks characteristically contain previous block’s data transaction, timestamp, and cryptographic hash. No central company or person owns blockchain. Instead, information is stored across many personal computers so that there’s no middleman. It’s distributed and decentralized in nature so that no one can corrupt it or take it down. However, anyone can utilize the system and help run it.

2- Major features of blockchain

Though for many people, the effectiveness of blockchain is unknown, it’s important to know if you’re planning to pursue a career in this disrupting technology. Let’s have a look at the key features of it.

2.1- Distributed ledger

All participants validate the information individually without any central authority. Each and every node contains indistinguishable copies of all the information.

2.2- Chronological

Each block comes with a unique timestamp which is the time when it was incorporated in the system. Timestamp acts like a variation for the hash function and no two blocks can contain the same timestamp. The timestamp is also used to evaluate whether to accept or deny a block.

2.3- Immutable

The records in the system are immutable which means the information on the system is safe and tamper proof.

2.4- Consensus-based

On a blockchain, a transaction has to be approved by each and every participant (node), else it’s rejected.

2.5- No need of a middleman

The members of the blockchain make sure that there’s no malpractice and thus there’s no need of a middleman to monitor and take care of the transactions.

2.6- Trustless operation

In the blockchain, the consensus ensures that no mal-intended or wrong transaction takes place and thus the operation is trustless in nature. So, wrong transactions don’t get validated and entered in the system.

3- What’s bitcoin?

Presently, we’re living in a huge technology expansion and one of this is certainly the most innovative product to finance – cryptocurrencies. Popularized by bitcoin, these virtual currencies utilize blockchain technology to process transactions.

Bitcoins have been gaining a huge amount of importance over the past few years. Let’s have a look at why these digital currencies are being accepted across the world.

3.1- Better acceptance

Today, more customers are using bitcoins because more legitimate companies and businesses are accepting them as a form of payment.

3.2- Control over capital

Many currencies and their usage outside of their native country are being restricted to an extent, thus increasing the demand for bitcoin.

3.3- Reduced remittance

Around the world, many governments are implementing policies that regulate remittance made from other countries either by writing new regulations or making the charges significantly high. This restriction of not being able to send money overseas is driving more people toward cryptocurrencies such as bitcoin.

3.4- Security

As we’ve discussed earlier, bitcoins use blockchain technology which is a solid and secure technology. Users of cryptocurrencies have already started to experience the benefits of using such a robust technology. This offering of a more secure way of transacting in our present ecosystem is a huge plus.

4- A look into blockchain’s future developments

Blockchain’s future developments will be mainly based on its robust built-in abilities. It’ll act like a tool for bringing everybody at the highest level of accountability. Here’re some major impacts of blockchain’s future developments.

4.1- Cybersecurity

In the blockchain, all information is verified and encrypted utilizing advanced cryptography, making the technology resistant to hacks and unauthorized changes. While centralized servers can be highly susceptible to hacking, human error, corruption or data loss, using a distributed, decentralized system like blockchain will allow data storage to be more robust and safe against attacks.

4.2- Internet of Things

There’re lots of systems like doorbells, buildings etc that are powered by Internet of Things. These systems are embedded with sensors, network connectivity, and software. However, as these systems operate from a centralized location, hackers can gain access to them. Blockchain comes with the potential to address these security concerns as it decentralizes all the information, which is becoming increasingly important together with the increase in IoT capabilities.

4.3- Healthcare

While patients’ medical information can be stored in a central location, this centralization of such personal information makes it highly vulnerable. With the huge amount of private information collected by healthcare providers, it’s necessary to have a secure platform.

With the emergence of blockchain and its implementation, healthcare organizations can create a secure database to store medical records and strictly share them with patients and authorized doctors.

4.4- Unified communications

With the implementation of blockchain, it’s possible to enable safer, faster and more reliable communications. Digital or automated communication based on pre-built algorithms is already taking place in some industries.

Implementation of blockchain can shift the entire landscape to allow authorized communications that occur more freely in the automated environment, thus enhancing the reliability and safety of the communications.

4.5- Making donations

People, who donate for noble causes, are often concerned about the fact that what percentage of their donation is truly being given to charities. Implementation of blockchain can ensure that these donations reach exactly where they actually needed to go.

Already, bitcoin-based charities are developing trust through smart contracts together with online reputation systems and letting donors see where their donations actually go through a transparent and secure ledger.

5- Artificial intelligence versus blockchain

Both artificial intelligence and blockchain are major trends of today’s world and are being talked about widely. A lot of implementation of AI can be seen today across industries – from advanced computer vision and machine translation to processing and analytics of huge datasets. Companies with adequate resources are already making use of this technology to improve their operational efficiency and increase profitability.

On the other hand, the emergence of blockchain that is equipped with distributed ledgers and advanced cryptographic tools. Popularized by bitcoin, blockchain is considered as one of the biggest innovations that have the ability to disrupt technology. Let’s have a look at a small AI-blockchain comparison to get a clear idea of the differences between these two technologies.

• Blockchain in distributed and decentralized in nature as opposed to AI’s more centralized infrastructure.
• For now, AI is like a black-box solution while blockchain tends to be more transparent in every transaction processed.
• While centralized providers own and operate a significant number of AI technologies, a lot of the blockchain players publish all their codebases as open-source code which can be inspected by anyone at any given point of time.
• AI is based on a lot of probabilistic formulas, while the blockchain is more deterministic by nature.

Presently, AI startups are being increasingly acquired by tech giants. These organizations rely on massive amounts of data for training their AI agents to gain a huge competitive advantage. Centralized AI leaves room for abuse like huge surveillance of people using computer-vision-powered technology and face recognition. Also, creating solutions based on a centralized environment needs organizations to hand over the control of their data to third parties.

6- Combining AI and blockchain

The concept of AI is heavily used for denoting computers which can work in projects where the intervention of human intelligence is required. Technologies like machine learning, artificial neural networks, deep learning etc make this possible.

Blockchain stores digital information in a distributed and encrypted manner. It allows developing a highly secured database that can store all the information in a structured manner and make it publicly available. While humans can teach computer algorithms to increase their capabilities, the developers of AI aren’t able to predict an AI system’s way of thinking.

Put simply, we can develop the algorithm that’ll teach the computer to analyze massive amounts of data, we cannot predict how that algorithm will develop. If an AI system’s decisions are recorded in the blockchain, we’ll receive the database and will be able to see the decision taken by the AI system and to explain their logic. It’ll also ensure the security of the information as the information stored in the blockchain cannot be altered.

Conclusion

Despite the benefits of merging AI and blockchain, there’re some challenges related to security that need to be taken care of in order to make the integration successful. However, uniting both these progressive technologies has the potential to revolutionize the way business is conducted across the globe.

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To learn about blockchain, click here and read our another article.

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