#2021Trends Archives

Machine Learning Career Paths: 8 Demanding Roles in 2021

Posted on 15/06/2021 by Anteelo Master

An Introduction to Machine Learning | DigitalOcean

In 2021, the focus on digitalization is as strong as ever before. Machine learning and AI help IT leaders and global enterprises to come out of the global pandemic with minimal loss. And the demand for professionals that know how to apply data science and ML techniques continues to grow.

In this post, you will find some career options that definitely will be in demand for decades to come. And there is a twist ― AI has stopped being an exclusively technical field. It is intertwined with law, philosophy, and social science, so we’ve included some professions from the humanities field as well.

Popular ML jobs to choose in 2021

What are the possible careers in machine learning? - Quora

Programmers and software engineers are some of the most desirable professionals of the last decade. AI and machine learning are no exception. We have conducted research to find out which professions are the most popular and what skills you need for each of them (based on data from Indeed.com and Glassdoor.com).

1. Machine learning software engineer

3 Fast Facts: What You Need to Know About Machine Learning as a Software Engineer | CodeIntelx

A machine learning software engineer is a programmer who is working in the field of artificial intelligence. Their task is to create algorithms that enable the machine to analyze input information and understand causal relationships between events. ML engineers also work on the improvement of such algorithms. To become an ML software engineer, you are required to have excellent logic, analytical thinking, and programming skills.

Employers usually expect ML software engineers to have a bachelor’s degree in computer science, engineering, mathematics, or a related field and at least 2 years of hands-on experience with the implementation of ML algorithms (can be obtained while learning). You need to be able to write code in one or more programming languages. You are expected to be familiar with relevant tools such as Flink, Spark, Sqoop, Flume, Kafka, or others.

2. Data scientist

Data Scientist Salary: Starting, Average, and Which States Pay Most

Data scientists apply machine learning algorithms and data analytics to work with big data. Quite often, they work with unstructured arrays of data that have to be cleaned and preprocessed. One of the main tasks of data scientists is to discover patterns in the data sets that can be used for predictive business intelligence. In order to successfully work as a data scientist, you need a strong mathematical background and the ability to concentrate on uncovering every small detail.

Bachelor’s degree in math, physics, statistics, or operations research is often required to work as a data scientist. You need to have strong Python and SQL skills and outstanding analytical skills. Data scientists often have to present their findings, so it is a plus if you have experience with data visualization tools (Google Charts, Tableau, Grafana, Chartist. js, FusionCharts) and excellent communication and PowerPoint skills.

3. AIOps engineer

Is AIOps the answer to DevOps teams' ops prayers?

AIOps (Artificial Intelligence for IT Operations) engineers help to develop and deploy machine learning algorithms that analyze IT data and boost the efficiency of IT operations. Middle and large-sized businesses dedicate a lot of human resources for real-time performance monitoring and anomaly detection. AI software engineering allows you to automate this process and optimize labor costs.

AIOps engineer is basically an operations role. Therefore, to be hired as an AIOps engineer, you need to have knowledge about areas like networking, cloud technologies, and security (and certifications are useful). Experience with using scripts for automation (Python, Go, shell scripts, etc) is quite necessary as well.

4. Cybersecurity analyst

A cybersecurity analyst identifies information security threats and risks of data leakages. They also implement measures to protect companies against information loss and ensure the safety and confidentiality of big data. It is important to protect this data from malicious use because AI systems are now ubiquitous.

Cybersecurity specialists often need to have a bachelor’s degree in a technical field and are expected to have general knowledge of security frameworks and areas like networking, operating systems, and software applications. Certifications like CEH, CASP+, GCED, or similar and experience in security-oriented competitions like CTFs and others are looked at favourably as well.

5. Cloud architect for ML

The majority of ML companies today prefer to save and process their data in the cloud because clouds are more reliable and scalable, This is especially important in machine learning, where machines have to deal with incredibly large amounts of data. Cloud architects are responsible for managing the cloud architecture in an organization. This profession is especially relevant as cloud technologies become more complex. Cloud computing architecture encompasses everything related to it, including ML software platforms, servers, storage, and networks.

Among useful skills for cloud architects are experience with architecting solutions in AWS and Azure and expertise with configuration management tools like Chef/Puppet/Ansible. You will need to be able to code in a language like Go and Python. Headhunters are also looking for expertise with monitoring tools like AppDynamics, Solarwinds, NewRelic, etc.

6. Computational linguist

IJCLNLP International Journal of Computational Linguistics and Natural Language Processing

Computational linguists take part in the creation of ML algorithms and programs used for developing online dictionaries, translating systems, virtual assistants, and robots. Computational linguists have a lot in common with machine learning engineers but they combine deep knowledge of linguistics with an understanding of how computer systems approach natural language processing.

Computational linguists frequently need to be able to write code in Python or other languages. They are also frequently required to show previous experience in the field of NLP, and employers expect them to provide valuable suggestions about new innovative approaches to NLP and product development.

7. Human-centered AI systems designer/researcher

Human-Centered Machine Learning. 7 steps to stay focused on the user… | by Jess Holbrook | Google Design | Medium

Human-centered artificial intelligence systems designers make sure that intelligent software is created with the end-user in mind. Human-centered AI must learn to collaborate with humans and continuously improve thanks to deep learning algorithms. This communication must be seamless and convenient for humans. A human-centered AI designer must possess not only technical knowledge but also understand cognitive science, computer science, psychology of communications, and UX/UI design.

Human-centered AI system designer is often a research-heavy position so candidates need to have or be in the process of obtaining a PhD degree in human-computer interaction, human-robot interaction, or a related field. They must provide a portfolio that features examples of research done in the field. They are often expected to have 1+ years of experience in AI or related fields.

8. Robotics engineer

An Overview of a Career as a Robotics Engineer |

A robotics engineer is someone that designs and builds robots and complex robotic systems. Robotics engineers must think about the mechanics of the future human assistant, envision how to assemble its electronic parts, and write software. Thus, to become a specialist in this field, you need to be well-versed in mechanics and electronics. Since robots frequently use artificial intelligence for things like dynamic interaction and obstacle avoidance, you will have plenty of opportunities to work with ML systems.

Employers usually require you to have a bachelor’s degree or higher in fields like computer science, engineering, robotics, and have experience with software development in programming language like C++ or Python. You also need to be familiar with hardware interfaces, including cameras, LiDAR, embedded controllers, and more.

Bonus: AI career is not only for techies

AI is NOT FOR THE TECHIES ALONE - Consulting Insight | Magazine for Consulting World | Management Consulting | Engineering Consulting

If you don’t have a technical background or want to transition to a completely new field, you can check out these emerging professions.

1. Data lawyer

Data lawyers are specialists that guarantee security and compliance with GDPR requirements to avoid millions of dollars in fines. They know how to properly protect data and also how to buy and sell this data in a way that avoids any legal complications. They also know how to manage risks arising from the processing and storing of data. Data lawyer is the professional of the future; they stand at the intersection of technology, ethics, and law.

2. AI ethicist

An AI ethicist is someone who conducts ethical audits of AI systems of companies and proposes a comprehensive strategy for improving non-technical aspects of AI. Their goal is to eliminate reputational, financial, and legal risks that AI adoption might pose to the organization. They also make sure that companies bear responsibility for their intelligent software.

3. Conversation designer

A conversation designer is someone who designs the user experience of a virtual assistant. This person is an efficient UX/UI copywriter and specialist in communication because it is up to them to translate the brand’s business requirements into a dialogue.

How much does an ML specialist make?

Machine Learning Engineer Salary | How Much Does an ML Engineer Earn? | Edureka

According to Indeed.com, salaries of ML specialists vary depending on their geographical location, role, and years of experience. However, on average an ML specialist in the USA makes around $150,00 per year. Top companies like eBay, Wish, Twitter, and AirBnB are ready to pay their developers from $200,000 to $335,000 per year.

At the time of writing, the highest paying cities in the USA are San Francisco with an average of $199,465 per year, Cupertino with $190,731, Austin with $171,757, and New York with $167,449.

Industries that require ML/AI experts

Today machine learning is used almost in every industry. However, there are industries that post more ML jobs than others:

Transportation. Self-driving vehicles starting from drones and ending up with fully autonomous vehicles rely very heavily on ML. Gartner expects that by 2025, autonomous vehicles will surround us everywhere and perform transportation operations with higher accuracy and efficiency than humans.
Healthcare. In diagnostics and drug discovery, machine learning systems allow to process huge amounts of data and detect patterns that would have been missed otherwise.
Finance. ML allows banks to enhance the security of their operations. When something goes wrong, AI-powered systems are able to identify anomalies in real-time and alert staff about potentially fraudulent transactions.
Manufacturing. In factories, AI-based machines help to automate quality control, packing, and other processes, while allowing human employees to engage in more meaningful work.
Marketing. Targeted marketing campaigns that involve a lot of customization to the needs of a particular client are reported to be much more effective across different spheres.

Women Who Created History in the Field of Programming

Posted on 11/06/2021 by Anteelo Master

Today, it is almost impossible for some people to believe that such a field as software programming was once almost exclusively a female field. What started as an unprestigious tedious profession done by women is now the field where large amounts of money circulate. As soon as programming started to be used for rocket science and became more prestigious, women were squeezed out not only from their working places but also from the history of programming. Test yourself: how many great women in computer science can you remember?

Let’s try to fix this injustice. Feel free to share the names of inspiring women in programming from your countries, and we’ll try to cover them in future articles!

Ada Lovelace

Women Who Created History in the Field of Programming

Augusta Ada King, Countess of Lovelace, was an English mathematician, writer, and the author of the first computer program as we know it today. She was born in the family of Lord and Lady Byron (yes, the Byron). However, she didn’t get to know her father, who left soon after she was born. Her mother, fed up with the romantic aspirations of her husband, did everything possible for Ada to grow up with a firm grounding in math and natural science. She was taught by the best teachers it was possible to find at that time.

Ever since she was a little girl, Ada was eager to learn and put her mind into inventions. For example, when she was twelve, she tried to construct mechanical wings so that she could fly. She approached the matter very scientifically, investigating different materials and how birds’ wings are constructed.

In 1833, she met Charles Babbage. He was working on a mechanical general-purpose computer that he called the Analytical Engine. Ada’s knowledge about technology and science enabled her to be the first one to recognize that the machine had application beyond pure calculations. She even wrote and published the first algorithm intended to be carried out by such a machine. That makes her the first computer programmer in history. The imperative programming language Ada was named in her honor and memory.

Hedy Lamarr

Hedy was a Hollywood actress, film producer, but also… an inventor! She was born in 1914 and had a 28-year career in cinema. What she also did was to invent an early version of frequency-hopping spread spectrum communication for torpedo guidance.

Hedy was born in an upper-class family of a pianist and a successful bank manager. She showed early interest in theater and films, but she also enjoyed walks with her father who was explaining to her how various technologies in the society functioned. This was basically all her formal training as an inventor, all the rest she had to learn by herself.

Hedy was a loner and spent most of her time on various hobbies and inventions. Among the few people who knew and supported her work was the aviation tycoon Howard Hughes. She helped him to improve the design of his airplanes, and he put his team of scientists and engineers at her disposal.

During World War II, Lamarr learned that radio-controlled torpedoes that were used back then were easy to set off course. So she thought of creating a frequency-hopping signal that could not be tracked or jammed. She asked her friend, composer and pianist George Antheil, to help her implement it. Together, they developed a device for doing that by synchronizing a miniaturized player-piano mechanism with radio signals. Much later, this system was used to develop WiFi, GPS, and Bluetooth technologies.

Kateryna Yushchenko

Kateryna Yushchenko was born in 1919 in Ukraine. She was the first woman in the USSR to obtain a Ph.D. in Physical and Mathematical Sciences in programming. But the path to this Ph.D. wasn’t easy.

In 1937, she was expelled from the university in Kyiv because her father was accused of being the ‘enemy of the nation’. She applied to several universities but, eventually, had to move to Uzbekistan and go to a university in Samarkand, where the accommodation and food were provided by the state. She studied math obsessively. But then, as you know, World War II happened. During the war, Yushchenko got a job in a factory where they produced sights for tanks. Only after the war ended could she return to Ukraine to finalize her degree there.

In 1950, she became a Senior Researcher at the Kyiv Institute of Mathematics and one of the programmers to work on MESM, one of the first computers in continental Europe.

Yushchenko created the Address Programming Language in 1955, which could use addresses in analogous ways as pointers. She wrote many books about address programming, and the ideas behind it have influenced multiple other programming languages.

Mary Allen Wilkes

Mary Allen Wilkes was born in 1937. This talented woman was one of the first programmers and the first person to use a personal computer in the home. Ever since a little girl, she dreamed of working in law. Growing up, however, she majored in philosophy and theology. But undeniable talent in mathematics led her to become a programmer and logic designer. Wilkes is best known for her work in connection to the LINC computer that many people call the ‘world’s first personal computer’.

In 1959-1960, she worked at MIT’s Lincoln Laboratory in Lexington, Massachusetts, programming for IBM 704 and IBM 709. These machines were a huge step forward: they were mass-produced, handled complex math, and could be fitted into one room. But they were not suited for home use. In comparison, LINC represented a box that could be transported much easier (however, still with the effort of two or more people). For that time, it was really ‘small’ as Wilkes calls it in her paper. Mary Wilkes worked on LINC from home and wrote LAP6, one of the earliest operating systems for personal computers, which was very sophisticated for her time.

LAP6 is an on-line system running on a 2048-word LINC which provides full facilities for text editing, automatic filing and file maintenance, and program preparation and assembly. It focuses on the preparation and editing of continuously displayed 23,040-character text strings (manuscripts) which can be positioned anywhere by the user and edited by simply adding and deleting lines as though working directly on an elastic scroll. Other features are available through a uniform command set which itself can be augmented by the user. — Mary Allen Wilkes, Washington University, St. Louis, Missouri

An Introduction to Big Data Analytics| What It Is & How It Works?

Posted on 15/12/202015/06/2021 by Anteelo Master

Big data is a term that describes datasets that are too large to be processed with the help of conventional tools and also is sometimes used to call a field of study that concerns those datasets. In this post, we will talk about the benefits of big data and how businesses can use it to succeed.

The six Vs of big data

Tourism Intelligence International – Big Data

Big data is often described with the help of six Vs. They allow us to better understand the nature of big data.

Volume

As it follows from the name, big data is used to refer to enormous amounts of information. We are talking about not gigabytes but terabytes ( 1,099,511,627,776 bytes) and petabytes (1,125,899,906,842,624 bytes) of data.

Velocity

Velocity means that big data should be processed fast, in a stream-like manner because it just keeps coming. For example, a single Jet engine generates more than 10 terabytes of data in 30 minutes of flight time. Now imagine how much data you would have to collect to research one small aero company. Data never stops growing, and every new day you have more information to process than yesterday. This is why working with big data is so complicated.

Variety

Big data is usually not homogeneous. For example, the data of an enterprise consists of its emails, documentation, support tickets, images, and photos, transaction records, etc. In order to derive any insights from this data, you need to classify and organize it first.

Value

The meaning that you extract from data using special tools must bring real value by serving a specific goal, be it improving customer experience or increasing sales. For example, data that can be used to analyze consumer behavior is valuable for your company because you can use the research results to make individualized offers.

Veracity

Veracity describes whether the data can be trusted. Hygiene of data in analytics is important because otherwise, you cannot guarantee the accuracy of your results.

Variability

Variability describes how fast and to what extent data under investigation is changing. This parameter is important because even small deviations in data can affect the results. If the variability is high, you will have to constantly check whether your conclusions are still valid.

Types of big data

Data analysts work with different types of big data:

Structured. If your data is structured, it means that it is already organized and convenient to work with. An example is data in Excel or SQL databases that is tagged in a standardized format and can be easily sorted, updated, and extracted.
Unstructured. Unstructured data does not have any pre-defined order. Google search results are an example of what unstructured data can look like: articles, e-books, videos, and images.
Semi-structured. Semi-structured data has been pre-processed but it doesn’t look like a ‘normal’ SQL database. It can contain some tags, such as data formats. JSON or XML files are examples of semi-structured data. Some tools for data analytics can work with them.
Quasi-structured. It is something in between unstructured and semi-structured data. An example is textual content with erratic data formats such as the information about what web pages a user visited and in what order.

Benefits of big data

Big data analytics allows you to look deeper into things.

Very often, important decisions in politics, production, or management are made based on personal opinions or unconfirmed facts. By analyzing data, you get objective insights into how things really are.

For example, big data analytics is now more and more widely used for rating employees for HR purposes. Imagine you want to make one of the managers a vice-president, but don’t know which to choose. Data analytics algorithms can analyze hundreds of parameters, such as when they start and finish their workday, what apps they use during the day, etc., to help you make this decision.

Big data analytics helps you to optimize your resources, perform better risk management, and be data-driven when setting business goals.

Big data challenges

Understanding big data is challenging. It seems that its possibilities are limitless, and, indeed, we have many great solutions that rely heavily on big data. A few of those are recommender systems on Netflix, YouTube, or Spotify that all of us know and love (or hate?). Often, we may not like their recommendations, but, in many cases, they are valuable.

Now let’s think about AI-systems that predict criminal behavior. They analyze profiles of criminals and regular people and can tell whether a person is likely at some point to commit a crime. These algorithms are reported to be quite effective.

However, their predictions are not as effective as to give them legal power, mostly because of the bias: algorithms are prone to make sexist or racist assumptions if the data is racist or sexist. You have probably heard about the first beauty contest judged by AI. None of the winners were black, probably, because the algorithm wasn’t trained on photos of black people. A similar fail happened with Google Photos that tagged two African-Americans as ‘gorillas’ ― for the same reason. This demonstrates how important the gender-race sensitivity perspective is when choosing data for analysis. We should improve not only the technology but also our way of thinking before we can create technologies that effectively ‘judge’ people.

How to use big data

How Brands Use Data - 5 Real World Examples | InfoClutch

If you want to benefit from the usage of big data, follow these steps:

Set a big data strategy

First, you need to set up a strategy. That means you need to identify what you want to achieve, for example, provide a better customer experience, improve sales, or improve your marketing strategy by learning more about the behavioral patterns of your clients. Your goal will define the tools and data you will use for your research.

Let’s say you want to study opinion polarity and brand awareness of your company. For that, you will conduct social analytics and process raw unstructured data from various social media and/or review websites like Facebook, Twitter, and Instagram. This type of analytics allows assessing brand awareness, measuring engagement, and seeing how word-of-mouth works for you.

In order to make the most out of your research, it is a good idea to assess the state of your company before analyzing. For example, you can collect the assumptions about your marketing strategy in social media and stats from different tools so that you can compare them with the results of your data-driven research and make conclusions.

Access and analyze the data

Once you have identified your goals and data sources, it is time to collect and analyze data. Very often, you have to preprocess it first so that machine learning algorithms could understand it.

By applying textual analysis, cluster analysis, predictive analytics, and other methods of data mining, you can extract valuable insights from the data.

Make data-driven decisions

Use what you have learned about your business or another area of study in practice. The data-driven approach is already adopted by many countries all around the world. Insights taken from data allow you to not miss important opportunities and manage your resources with maximum efficiency.

Big data use cases

Let us now see how big data is used to benefit real companies.

Product development

When you develop a new product, you can trust your guts or rely on statistics and numbers. P&G chose the second option and spends more than two billion dollars every year on R&D. They utilize big data as a springboard for new ideas. For example, they aggregate and filter external data, such as comments and news mentions, using Bayesian analysis on P&G’s product and brand data in real-time to develop new products and improve existing ones.

Predictive maintenance

Even a minor mistake or failure in the oil and gas industry can be lethal and cost millions of dollars. Predictive maintenance with the help of big data includes vibration analysis, oil analysis, and equipment observation. One of the providers of such software is Oracle. Their machine learning algorithms can analyze and optimize the use of high-value machinery that manufactures, transports, generates, or refines products.

Fraud and compliance

Digitalization of financial operations can prevent credit card theft, money laundering, and other such crimes. The USA Internal Revenue Service is one of the institutions that rely on processing massive amounts of transactions with the help of big data analytics to uncover fraudulent activities. They use neural network models with more than 600 different variables to be able to detect suspicious activities.

Last but not least

Big data is the technology that will continue to grow and develop. If you want to learn more about big data, machine learning, and artificial intelligence in research and business, follow us on Twitter and Medium and continue reading our blog.

A Digital Divide has emerged as a result of Remote Working

Posted on 15/11/202015/06/2021 by Anteelo Master

Like many others, my family and I have done our best to enjoy the unexpectedly large amount of time we have together at home due to social distancing guidelines. Adjusting to the new normal, we have relied heavily on Internet access not only for work and school, but to stay sane and keep the peace. My wife and I both continue to work from home, frequently videoconferencing and collaborating with colleagues. The kids finished the school year online and now they are starting the new school year with a mixed arrangement of physical and virtual learning. Many hours of streaming video have been consumed. This isn’t an experience we want to repeat, but I believe it would have been far more difficult and stressful if we lacked the connectivity needed to remain productive, informed, and entertained during these times. Without that high-speed connection to the digital realm, this experience would feel more like we were stranded in a country where we didn’t speak the language — surrounded by activity yet unable to participate. It would create the very real feeling of “looking in from the outside.” The rapid onset of social distancing or stay-at-home measures has created just this feeling for a large number of people. Across the world, many people were suddenly thrust into unfamiliar remote working situations. And with the global percentage of households connected to the internet at only 55%, many organizations, in turn, discovered a digital divide that needed to be bridged for some employees. For example, some companies successfully stood up the infrastructure and processes necessary to support new remote capabilities, only to find that some of their employees lacked the connectivity or technological proficiency to be productive remote workers.

These current circumstances have placed the digital divide –not always apparent to many companies previously — into sharp relief. They’ve shown that digital life skills and work skills – not to mention the access to technology and connectivity needed to enable those skills — are as essential to us now as hunting and horseback riding were to our ancestors. Like STEM education, an emphasis on digital skill-building could help many people be more productive and could provide them a better work environment, more income, and a brighter future.

Infrastructure and processes to bridge the digital divide

Other related questions around remote work abound, especially in terms of corporate infrastructure. Would employees be able to use devices they already owned to perform their jobs, or would they need to be supplied with equipment? Where would that come from and how would it be prepared to access corporate data? And many were unprepared and unsure if their network was up to the task when demand suddenly shifted from inside the enterprise to requests from remote workers.

Processes were another big issue. In addition to addressing where we work, enterprises have had to consider how we work. What tasks does a company perform that must continue and could those be adapted for remote access? For some, the work needed to turn this into a distributed, remote work process was well documented. Team-oriented jobs, however, required more reengineering and may not have been as well defined.

Remote working: temporary or permanent?

Over the past few months, we have been helping our global enterprise customers adopt to this new environment and discussing the future of workplace. Many are debating whether remote work is a temporary fix or a permanent shift. In every case, I’m sure they will be reflecting on this experience and its challenges – and the digital divide in particular – to help them improve their resilience and that of their employees. These lessons will heavily influence the investments they make going forward in all areas of technology, training and business process reengineering.

Technology Trends to Watch in 2021 and Beyond

Posted on 15/09/202010/06/2021 by Tushar

There are some technology trends that fizz out over time and then there are the latest technology trends that stay on the sidelines and then gain traction after it either gets major funding or an industry suddenly integrates it in their process.

There are many such technologies that have already made their prominent mark in the previous years and are only a few more advances away from becoming mainstream. These technologies are the ones that we have listed as the latest and upcoming and latest technologies that would be setting a global trend in the coming years.

Without further ado, here are the ten technology trends for the future years –

1. Artificial Intelligence

Artificial Intelligence (AI) is constantly making its place in the list of top tech trends since quite some years. It has found a place in the future technology trends 2021 technology predictions as well.

AI is already known for its superiority in image and speech recognition, voice assistants, navigation apps, automation, and whatnot.

In the artificial intelligence trends 2021, we hope to see tremendous demand and rapid development of AI and modern industrial automation technology. As manufacturing and supply chains are getting back to full activity, labor shortage will turn into a serious issue. Automation with the assistance of AI, robotics and IoT will be a key solution to operate manufacturing.

Other than that AI development companies will be utilizing AI further to analyze interactions to decide basic underlying influences and insights to predict demand for services such as healthcare sector empowering specialists to make better choices about resource use, and to identify the changing ways of customer behavior by dissecting data in near real-time, driving incomes and improving customized experiences.

Its present-day tasks of enabling computers to read (studying messages and reports), see (through facial recognition), listen (by enabling Amazon Echo to answer your command), speak (Siri being able to give you an answer), and even record emotions (through affective computing), will help AI become a technology that no longer needs human intervention to aid its learning. The mass usage of AI in the world is leading to its adoption in a number of sectors: Customer Experience, Designing, Healthcare, Fintech.

2. Voice search

The trending technologies in 2021 will remain incomplete without voice search technology. The time is long gone when the only relation between voice and technology was to talk using our mobile devices. The importance of voice search in today’s time cannot be ignored, as this emerging technology trends 2021 uses speech recognition to identify what a user is saying with high accuracy. As a reply, it delivers the message to the user through voice.

While Google Action SDK and Integration of Siri into apps have already acquainted us with the power of voice in conducting everyday tasks, the applications have still remained very limited. But the future that it has set for itself, has placed voice based applications in the list of top technologies in 2021.

The near future with a set of upcoming new tech trends will find itself conducting more operations with a voice command. In only a matter of time, every device that we have surrounded ourselves with will be able to function and perform actions with a command of our voice. Mixed with Natural Language Processing, AI, and Machine Learning with voice-based applications will find a greater place in users’ everyday activities.

3. Natural language processing

In a year or two, Chatbots would have reached their market potential, with the majority of the businesses employing them to redefine their customer engagement policies. However, the new technology in software development would reach a whole different level through the integration of Natural Language Processing (NLP).

Presently NLP is broadly utilized in financial marketing. It shares a thorough insight into market sentiments, tender delays and closings, and obtains data from huge repositories.

The requirement for a semantic search is another top tech trend to affect NLP in 2021. This search would draw both NLP and NLU (Natural Language Understanding) requiring a granular perception of the central thoughts contained inside the text.

E-retailers would utilize NLP and Machine Learning methods to build customer engagement, analyze their browsing patterns and shopping trends.

A report by Business Insider predicts that the chatbot market which was worth $2.6 billion in 2019 will reach US$9.4 billion by 2024.

4. Blockchain

The rate at which Blockchain is growing has placed it at a pivotal point in the list of latest trends in technology. While prevalent with cryptocurrencies currently, the world will see its mass adoption in the coming years, and Blockchain technology trends will definitely stay here.

Cryptocurrencies, the important Blockchain element, will also find itself divided in a number of currencies and would be floated in the market just like fiat currencies. People who are currently unaware of what Blockchain and Cryptocurrencies are and where they can spend them, will start doing their everyday transactions with them. The future will see Blockchain development solution being explored beyond cryptocurrencies.

In 2021, the technology will scale with updates and better implementations of smart contracts. When it comes to securely exchanging data, the updates to Blockchain tech may mean that it can be used whenever and wherever there is a case of secure, immutable data exchange. For that people can opt for companies like blockchain development company in USA to avail the benefit of this technology.

According to reports by Markets and Markets, the global blockchain market size is expected to grow from USD 3.0 billion in 2020 to USD 39.7 billion by 2025, at an impressive CAGR of 67.3% during 2020–2025.

5. Internet of Things

Internet of Things (IoT) has been finding itself in the list of latest app development trends 2021. Our homes that already have a series of smart products such as TVs, water heaters, microwaves, yoga mats, and the voice-enabled personal assistants like Amazon echo, etc., will find a series of new entries in only a matter of time.

The Internet of Things is the future, and has facilitated devices such as home appliances, cars and much more to be connected to and exchange data over the Internet. The IoT technology trend enables better security, efficient decision making for businesses as information is collected and analyzed through the internet. It is said to provide predictive maintenance, speed up medical care, improve customer service, and offer various benefits in the coming future.

According to Statista Forecasts, around 50 billion of IoT devices will be in use around the world, creating a massive web of interconnected devices spanning everything from smartphones to kitchen appliances.

6. Edge computing

One of the least talked about but one of the latest upcoming technologies will take the center stage the day IoT becomes a mainstream technology. While businesses are presently working comfortably in their Cloud setup, things are going to change pretty soon with edge computing.

Edge computing supports computation and data storage together on the collection device, inplace of depending on one major location that can be miles away.

It is a computing element where everything from – information processing, content collection, and delivery etc. are situated close to the source of information. Latency and connectivity challenges, bandwidth restrictions, and higher functionality are some of the benefits that get embedded at the edge of the source.

To be a part of the future technology trends enterprises should start using edge based design patterns in the infrastructure architectures, especially in those that come with notable IoT elements. For achieving this, the starting point can use edge-specific and co-location networking abilities.

Being a prominent part of a new technology, we with our IoT app development company are expected to witness increased attention being paid to edge computing for enabling intelligent networks, in which the connected devices will be performing the necessary analytics right at the location and would use the results for performing the specific actions. It will all happen within a few milliseconds, as compared to a few hundred milliseconds – the time it takes today with cloud computing. The promise that edge computing comes with, makes it an important addition in the top 10 new technology trends.

7. Predictive & personalized medicine

Innovation is pushing the healthcare sector forward at a growing rate. The potential to get information about an individual’s lifestyle from a smartwatch (like Samsung, Apple Watch, Fitbit, etc.) is giving healthcare professionals the ability to forecast and even treat possible medical issues before a patient even has any symptoms.

When it comes to treating these patients, this technology trend will help doctors prescribe more personalized medicine, often referred to as predictive medicine. The info-driven understanding of how useful some treatments are on people will boost the healthcare market forward in 2021.

With regards to treating the patients, this technology trend will assist doctors in prescribing personalized medication, generally known predictive medicine. The data driven comprehension of how helpful treatments are on individuals will support the medical services market in the coming years.

Reports from Juniper Research reveal that wearables, including health trackers and remote patient monitoring devices, will be the must haves in delivering healthcare with the forecasted annual spend of $20 billion on these devices by 2023.

8. 5G

The 2021 technology predictions show that the world in the coming years will be under the spell of fast internet connection and all the various perks that come attached with it.

5g will find its place in the market very soon, bringing with itself benefits such as high internet speed, lower latency, and higher capacities.

The benefits would make it possible for autonomous vehicles and wireless VR to work with minimal technical hesitancies. The benefit of 5G would be two-fold. Not only would it enrich the user experience by offering higher data rates when we talk by VR or AR, but it will also be much safer, which is where the latency point comes in.

But just how fast would 5G be? It is anticipated that when it takes 26 hours for a movie to download in a 3G network, in 5G the time would reduce to 3.6 seconds.

9. Facial recognition

Your face will become the ideal technology partner by 2020. What started with iPhone X with its face ID will be taken ahead by a number of industries who would now be using your face for a series of different applications.

From the present ability to unlock your phone just by looking at it, the latest technology in facial recognition will allow you to unlock your house and car, which would be connected with the internet. In the future it is said that an individual would be able to withdraw money from the ATM using nothing but your face.

10. Automation

Automation has moved ahead from its involvement in the production lines. There are a number of ways automation will create its presence around us by becoming one the most awaited technologies.

From packaging juice boxes to delivering pizza in self-driving vehicles, automation is fast climbing the food chain from doing routine tasks to a lot more complex, decision-making tasks.

Mixed with Robotics, Machine Learning, and Artificial Intelligence, processes across multiple industries like Food and Beverage, Medical, and Customer Service will become further streamlined and automated by the time future hits our doors.

So here were the latest technology trends that will see the mass adoption in the future years. Are you ready for them?

The Three Technology Entrants That Will Additionally Redefine the World in the Upcoming Years

1. Big data

With the ever growing amount of interaction between machines and humans, the devices that we carry with us every waking hour have become a massive repository of data. Data that is waiting to be converted into meaningful information and insight for businesses to use for offering better service.

2. AR/VR

If there is anything that Pokémon Go has taught us it is that users are very open to the idea of losing the sense of their reality for a good time in the virtual world. Taking the cue from the interest that Pokémon Go managed to create, a number of business models have come up in a number of different industries like Healthcare, Retail, and Education etc. focusing on giving the users a chance to introduce fiction in their daily life and find something of value at the back of the whole experience. That’s why AR VR app development company like us help the clients to achieve their technological goals.

3. Chatbots

It has already been established that Chatbots are shaping the business growth story by making business available 24*7. The coming years will find the conversion magnet becoming more personal and intelligent with the power of machine learning and predictive analytics technologies.

Frequently Asked Questions

Q. Which technology is best in future?

Artificial Intelligence in the best technology for the future. The technology, with its potential to mimic human brain, has offered a myriad of opportunities/functionalities to different business verticals, such as Real Estate, Healthcare, Education, Travel, Finance, and more.

Q. What are future technology trends?

While there are various technologies that exist in the market and will continue to disrupt the market, here are a few that are newly entering the market and are poised to bring transformation:-

5G technology
Edge Computing
Prescriptive Analysis