Artificial Intelligence Trends To Watch Out For

10 Artificial Intelligence Trends To Watch Out For In 2021

What does the future hold with respect to artificial intelligence?

In this day and age, you’d be hard pressed to find any sector of the economy where artificial intelligence hasn’t yet set foot. In fact, AI is so integrated into our daily lives that most don’t even notice it.

One sector experiencing fast pace AI adoption is the retail industry.

In a recent report by Fortune Business Insights, 80% of business executives claimed that their businesses will integrate artificial intelligence into their products and services lines between 2020 and 2027.

Ready to launch your Data Science career with UDEMY? Get started TODAY for just $9.99 (95% OFF) with my link below:

So what are some of the trending applications of AI in 2021 that we should look out for?

In this article, we are going to look at the top 10 trends in AI that will characterize the year 2021 that I derived from these 80 AI & ML expert predictions.

Some trends like conversational artificial intelligence, implemented through natural language processing, have been around for a while and you can see this from the heavy use of chatbots for customer support.

However, there are some new entrants, like hyper automation, which uses robotic process automation to take automation to a new level.

Either way, the next 12 months will be a very interesting period for AI innovators and investors as we expect louder talks on Ethical AI and policies enacted by lawmakers to control AI implementations. What would be a better time to learn AI then?

So without much further ado, let’s get to the top AI trends in 2021 so you stay in the know of what to expect this year.

If you’d rather read this visually then I compressed the whole article into a pretty infographic below. Feel free to copy and paste the embed code below if you’d like to publish it on your site.

Let’s get started.

Top AI Trends 2021 [Infographic]

Publish This Infographic On Your Site

1. AI and Cyber Security

Cybersecurity is one of the areas where artificial intelligence has been applied to a great extent with a relatively high degree of success.

Through AI and ML, algorithms that can use training data to learn how to respond to different situations have been developed. They develop their threat intelligence by recognizing patterns in data and learning from past experiences. 

One of the main reasons AI has been embraced to fight cybersecurity today is the limitations that a human team faces. 

Manual threat hunting is expensive, tedious and time-consuming. By implementing AI in cybersecurity, some attacks that could have gone undetected are now thwarted.

But automating detection has also led to false positives, but that’s a topic for another day. Or you could read this other article on the impact of AI on cybersecurity to find out more.

In addition to that, traditional cybersecurity has a reactive nature, where the action is only taken after a breach has occurred. But by implementing machine learning, cybersecurity experts are now able to predict attacks before they occur and take precautionary measures.

According to Ponemon Institute, the cost of recovery from a typical data breach is $ 3.86 million. 

It is for this reason that companies and businesses have invested more in AI to avoid these unnecessary losses of time and resources.

Cybersecurity will therefore remain one of the trending topics among AI enthusiasts for the foreseeable future.

2. Natural Language Processing

I once went to a company support website, launched their chat window and started asking a few questions about the product I was about to buy.

The conversation went rather smoothly.

I then asked a question, whose answer surprised me. The response was that for that kind of question, I needed to talk to a human support staff. So all this while I have been talking to a bot. And not just me, in 2012 Eugene Goostman also fooled a team of judges into thinking it was human when it was actually a bot.

Well, conversational artificial intelligence refers to technologies like chatbots or even a voice assistant that a user can actually talk to.

Through machine learning and natural language processing, these applications are able to use large amounts to learn and thereby imitate human interaction, recognize text inputs, voice inputs and translate them across various languages.

Conversational AI has become a trend in artificial intelligence because it has had a broad stretch of use cases that has proved lucrative for businesses and enterprises, making them more profitable. In fact, the projected value of chatbot eCommerce transactions is expected to be $ 112B by 2023.

While what comes to mind when we talk about conversational AI is chatbots, there are many other areas that this technology has been applied with success. For example, online customer support used chatbots to answer common questions like shipping and cross-selling products.

At this point in time, I still think most AI chatbots can only offer rudimentary problem-solving.

However, they have proven to help reduce time and improve the efficiency of repetitive customer support interactions. These freed-up resources can therefore be directed to other more involving customer support interactions.

3. AIOps

What is artificial intelligence for IT operations?

AIOps is the application of artificial intelligence that uses machine learning and big data analytics to simplify IT operations management while accelerating and automating problem resolutions in complex environments.

With the radical nature of changes in IT, traditional IT management techniques have failed to cope with the current digital transformations in most businesses.

So AIOps has come in to see through the implementation of significant changes in ITOps procedures, leading to a great restructuring of how we currently manage IT ecosystems.

AIOps has remained a trending topic in AI because, since its inception, organizations have exponentially increased their interest in and adoption of AIOps.

The AIOps Market was valued at USD 13.51 billion in 2020, and is projected to be worth USD 40.91 billion by 2026, registering a CAGR of approximately 21.05% during the forecast period of 2021-2026.

Thanks to AIOps, organizations have enabled innovation, fended off disruptors as well as managed big data that is characteristic of most online business. Here is a guide on AIOps adoption by Gartner.

At the core of AIOps is machine learning. 

Once the big data is aggregated from siloed tools, machine learning is used analyze to this huge amount of diverse data, something that was not previously possible by manual human effort.

IT is moving beyond human scale and by understanding that this is one of the major drivers of AIOps, organizations accept that their tooling needs to adapt.

So the automation of business processes through technology, to be specific AIOps, will lead to lower costs, speed increases and fewer errors, while at the same time freeing up the manpower for other priority, higher-level achievements.

4. AI and IoT

When you combine artificial intelligence (AI) and the internet of things (IoT), you get AIoT, or artificial intelligence of things, to be more precise.

While you probably already know what AI is, what is IoT?

Let me paint the picture.

When you have a refrigerator, a wearable device, a sensor or any other digital device connected to the internet, and that these devices can be detected by other devices, can collect and process data, then you have the internet of things.

Now, if you add AI to the mix, it means that these devices can analyze this collected data, make sense of it and consequently act on it without human intervention.

The use of AI in IoT has become a major trend among developers today because by making these devices smart optimal systems can be built that are high performance. A technology trends survey by SADA confirms this.

In fact, the global Internet of Things (IoT) market size stood at USD 250.72 billion in 2019 and is projected to reach USD 1.463 trillion by 2027 according to Fortune Business Insights.

Let’s look at an example.

Artificial intelligence and the internet of things can intersect in a smart office building. You choose to install a network of smart environmental sensors in the building. These sensors can then gather data that tells who many personnel are present.

With this data, they can adjust the lighting and heating accordingly, thereby improving energy efficiency.

So as you can see AIoT can benefit both the business and the user.

5. Ethical AI

While artificial intelligence is a very interesting and exciting topic, it has been taken differently depending on who you ask.

In some circles you’ll see comments like we are giving too much decision making power to machines, we are giving away our jobs to machines, how much should we let AI do?

If you’ve been up to speed with technology, then you’ll admit that AI has helped a great deal in optimizing logistics, detecting fraud, conducting research, making translations, composing art, all these transforming our lives for the better.

However, machine morality is a concept in AI that data scientists have been exploring for as long as AI has existed, and thanks to AI accidents like this Uber self-driving car, talks about ethics and AI has been trending highly lately and is a major topic of discussion in 2021.

One good example is a racist robot.

The speed and capability of AI notwithstanding, it cannot be trusted to always be fair and neutral.

Google, one of the major players in the artificial intelligence area has software that is able to identify faces, objects and scenes in your photos and classify them. However, these recently blew up when similar software that was used to predict future criminals showed a bias against black people.

Now, while I want to go all-in with feet on the gas and blast AI, if you take a moment you’ll realize that these applications are built by humans.

If the human building the app already has the bias in them, who’s to blame when something like this happens: the machine or the human?

It’s for this reason and many others that talks around ethical ethical AI will dominate discussions among 

6. AI and Cloud Computing

What is the role of AI in cloud computing?

First of all, cloud computing refers to the delivery of computing services like servers, storage, database, networking, analytics and intelligence over the internet.

While there are already a ton of areas in our lives today where artificial intelligence and cloud have met, like through Siri, Amazon Alexa and Google Home, most of us don’t even realize that it is a customer blend of both AI and cloud computing.

IN the year 2020, total cloud infrastructure services spending was US$142 billion which means that there is a high demand for cloud computing services, and part of this has been thanks to the incorporation of AI in cloud computing.

One of the main challenges to implementing AI is the high initial costs involved, like in the purchase of servers and access to datasets.

Now cloud computing has become a hot topic among AI enthusiasts because it makes it easier for innovators to benefit from AI technology even if they lack access to their own supercomputers with super processing power, huge datasets or top tech talent.

I’d say that cloud computing is democratizing access to AI.

By rolling out AI tools as a software as a service (SAAS) platform, companies get the ability to use AI now, without having to make big upfront investments.

In fact, this survey by Delloite on AI adoption is proof that cloud based software and platforms are helping early adopters benefit from artificial intelligence even if they don’t have the expertise to train their systems and manage their data.

And there’s more.

Another interesting convergence of AI and cloud computing is through cloud based enterprise software that integrates AI out of the box. 

So moving forward, the main trends in AI with respect to cloud computing will be around infrustructure as a service (IaaS), platform as a service (PaaS) and software as a service (SaaS).

7. HyperAutomation

If you’ve been reading about artificial intelligence trends lately then hyperautomation might not be a completely new term.

But what exactly is hyperautomation and what does it do?

Hyperautomation refers to the application of advanced technologies like robotic process automation, machine learning and artificial intelligence to automate process tasks and processes that were once done by humans.

The main target or “victim” of hyperautomation are the repetitive human tasks.

But it gets even more complex in the sense that the automation itself is automated. Means? Well, it means the business processes that need automation are dynamically discovered, and then bots to automate these processes are developed.

Now that’s HYPERautomation.

While typical use cases of hyperautomation are still in their early stages, it has become a trend in 2021 that is being utilized by businesses to accelerate, streamline and redesign processes. In fact, 78% of those who have already implemented RPA expect to significantly increase investment in RPA over the next three years according to Delloite.

One application of hyperautomation would be at a call center.

Let’s say a customer calls in. In an ordinary scenario, the support staff will need to pull up information from different systems in order to build the complete customer profile.

Now by using robotic process automation and artificial intelligence, the whole process can be automated to save time and improve efficiency.

How?

First by automating actions like mouse clicks and application launch, the system will easily pull up information about the customer from the respective systems without the agent having to switch between several applications.

But as you can see, hyperautomation does not completely eliminate the human from the process. 

A human still has to be there to harness this superpower.

8. AI and Healthcare

How is artificial intelligence and robotics transforming healthcare?

AI has been used in the healthcare industry for a long time, but it’s use has been catapulted by the recent COVID-19 pandemic that has vindicated the power of AI in various aspects of healthcare, like diagnostics and drug discovery.

If you let me jog your memory a bit, the use of AI in diagnosis and treatment of deseases has been an area of focus since the 1970’s when MYCIN was developed at Stanford for diagnosing blood-borne bacterial infections.

Even though it was not adopted for clinical use at that time, it was a light bulb moment in the possibility of use of AI in diagnosis and treatement of desease, especially considering misdiagnosis and medical error account for 10% of deaths in 2015 in the US alone.

Now fast forward to 2021 and voila, AI in its more potent form has been used to predict and diagnose desease faster than a bunch of medical professionals working together.

Here’s proof.

One study proved that an AI model using machine & deep learning algorithms was able to diagnose breast cancer faster than 11 pathologists.

Diagnostics is an area of healthcare that is the target of attention and investment in research with respect to AI.

Think about it.

Many diagnostic processes still rely on physical tissues obtained through biopsies as samples for medical examination. Handling of which present along with it risks such as the potential of infection.

One major trend in AI today is the focus on the ability to use the next generation of radiology tools to accurately and thoroughly perform diagnosis without the need for tissue samples.

This is just the tip of the iceberg of what AI has in store for the healthcare sector, rightfully so because Statista estimates that the global market size for artificial intelligence in healthcare for 2025 will be more than 28 billion U.S. dollars.

9. Autonomous Cars

If you mentioned self driving cars two decades, it would have sounded more like an artificial intelligence fanboy, full of himself.

But what for once seemed like a futuristic dream has turned into a modern day reality. And as artificial intelligence and innovative memory technologies mature, the personal and public transport sector will forever be transformed.

One of the main drivers of investment into artificial intelligence for autonomous vehicles is the desire to banish dangerous driving by taking humans out of the equation.

And you can rest assured that this trend is not dieing down anytime soon.

Check this out.

According to the National Highway Traffic Safety Administration (NHTSA), about 40,000 died on the roads in 2017, and nearly 90% of those accidents were caused by human error.

On paper, autonomous cars are able to drive better than humans because of the high performance super computers that they possess that run artificial intelligence and deep neural network algorithms.

In addition to this processing power, autonomous cars are equipped with multiple sensors like cameras and radars that facilitate path planning by enabling them understand their surroundings better, thanks to the massive amounts of data that they generate.

But how much is this going to cost?

While there are a few self driving cars already on the roads, successfully building a self driving car is by no means an easy feat and is a reserver for the high and mighty. No I mean, the big tech like Tesla.

Coming to think of it, a self driving car is projected to contain more lines of code than any software every build… so it should be bigger than Windows 10 OS source code, with more than 300 million lines of code, at least 1TB of storage, not forgetting that it needs a memory bandwidth of more than 1TB/seond to support the computing performance of their supercomputers.

Despite the reservations by some and the high production costs, the global autonomous car market is expected to grow from $5.6 billion in 2018 to $60 billion in 2030.

10. Computer Vision

What is computer vision?

A computer with vision glasses.

Nah, not quite but you are close. Let’s find out what computer vision is and what it has to do with trends in artificial intelligence in 2021.

Simply put, computer vision is a field of computer science that focuses on training computers to interpret and understand the visual world in the exact same way that a human would do.

How?

By implementing AI, deep learning and neural networks, a computer is able to identify and locate objects in images and videos taken from digital cameras and deep learning models, then react to what they see with a relatively high degree of accuracy. Sometimes being even more accurate than a human.

While the term computer visions might foreign some, applications of this AI technology has permeated various fields that we don’t even realize.

For example, talk of the Google Translate app.

Did you know that if you wanted to read signs in a foreign language all you need to do is point your phone camera at the words and Google Translate will instantly tell you what they mean in your language of choice?

Many other areas that are already using computer vision include autonomous cars, which we just talked of, healthcare, agriculture, manufacturing etc.

It is because of its broad spectrum of use that computer vision has received a lot of attention in terms of development of research and development of open source frameworks as well as incorporation into end products like smartphone face recognition.

So now you can see why the global computer vision market size was valued at USD 10.6 billion in 2019 and is expected to grow to USD 19.1 billion by 2027 according to Grand View Research.

Conclusion

This is just a tip of the iceberg on what is cooking with regards to AI today.

Despite the pros and cons, the artificial intelligence market is quickly and  steadily growing. It is even projected that the AI market value might reach $USD 190.61 billion by 2025

What does this mean for an artificial intelligence engineer?

Since companies will continue to implement AI, there has been and there will continue to be an increasing demand for experienced artificial intelligence and machine learning engineers to develop and maintain these solutions.

So what would be a better time to get started learning AI than today?

With these FREE AI RESOURCES that I put together, you can very easily get started today and set yourself on the path to a bright career and future.

Through these materials you’ll learn all the theory you need to start building your own machine learning models. You’ll start with the basics in algebra and calculus, then do some statististics because you need analytics skills, learn to code and then jump with both feet into ML models.

In addition to that, I have also written a guide to help you get your first job as a data scientist, because this is usually the most difficult part after learning to code.

I hope this article has opened your eyes to some of the game-changing AI applications that might become realities in the next few months.

What are some trends in AI that I didn’t mention in this article that you think really deserve a mention?

Please share your thoughts in the comments below.

Leave a Comment

Your email address will not be published. Required fields are marked *