What is Artificial General Intelligence?

Artificial General Intelligence vs Artificial Intelligence: What’s the Difference?

How Does General Artificial Intelligence Work?

Technologies that Drive Artificial General Intelligence

1. Robotics

2. Natural Language Processing

3. Deep Learning

4. Computer Vision

5. Generative AI

The Challenge Ahead

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Google is testing a product(Genesis AI) that uses Artificial Intelligence technology to produce news stories. Now the company is pitching that AI product to the news organization as stated by sources familiar with the matter. News Organizations such as The New York Times, The Washington Post and The Wall Street Journal, News Corp are amongst the ones that Google is in contact with.

Genesis is the rumored name of the tool, given by internal people working on it. It can take in information and produce news material (for example, specifics of current events). It was specified by the people on the condition to keep anonymity.

Google believes Genesis could serve as a personal assistant to journalists by saving their time to focus on other important tasks. The company believed it to be a responsible technology that might help lead the publishing industry away from the threats of generative AI.

Some Comments on the Topic

Should News Organizations Use AI?

The post Google is Working on an AI Tool ‘Genesis’ for Journalists Rumored appeared first on Tech Research Online.

But, there’s another interesting variation of IoT in today’s digital world. The Artificial Intelligence of Things (AIoT) combines IoT and AI to give birth to one of the smartest ways to delegate mundane, quick tasks… without needing human effort. There are plenty of exciting things to talk about. But, let’s see how AI and IoT function. 

Understanding the Basic Functionality of Artificial Intelligence & the Internet of Things.

1. IoT – An Overview

The Internet of Things is a system of objects, devices, or sensors that use software and technology to make a network for sharing data. This data can be used to perform certain functions or create chain reactions based on what you need the devices to do. IoT can be applied to different industries and even make your home appliances smarter and automate most of your daily tasks. 

2. AI – An Overview

Artificial intelligence combines mountains of data to process it using algorithms. These algorithms find patterns and specific features from that information to learn and perform any action it is programmed for. 

A common misconception people have about AI is that it can learn anything and everything to cause harm to humanity. That does become a possibility if the creator chooses to make an AI as powerful as that. However, that’s where the ethics of AI come to place. For now, let’s stick to the AI that helps you organize your inventory. 

Artificial Intelligence of Things – Reimagining IoT

As AI functions mainly off of data, and algorithms, it is a match made in heaven when you combine it with IoT. Its interconnected data exchange helps AI algorithms learn specific information more efficiently. Then, AI automates the basic functions of the devices with improved machine learning-based decision-making. It also enables IoT to analyze data, learn and make decisions without the need for a human. 

1) How Does AIoT Work?

The basic framework of IoT is supported by 4 components: Sensors/Devices, Connectivity, Data Processing, and User Interface. Here, the sensors collect information and share it with other devices using connectivity. The data is then processed and delivered to the user via a user interface. 

When we add AI into the mix, it becomes the 5th component. We introduce AI into IoT by using chipsets and hardware that enable AI, along with APIs and software solutions to use Artificial Intelligence tools. And with edge computing, IoT devices can process data as soon as it is gathered; instead of sending it to a software. This shrinks the bandwidth you need to move that data

2) Applications Benefits of Integrating AI in IoT

AIoT opens the door to seamless data processing and connectivity. This in turn reflects into several possible applications. 

• Camera systems at stores can recognize the faces of customers and use that data to ensure they do their checkout before leaving. Additionally, projects like Amazon Go can benefit from AIoT for seamless deductions from e-wallets of shoppers
• Smart home appliances can learn the basic habits of the user and automate tasks and suggestions accordingly
• Equipment maintenance becomes easier with predicted outcomes and automatic schedules as a result of consistent checks and reports
• Autonomous vehicles can use sensory data from internal and external devices to improve road safety. An example could be Teslas smart cars
• IoT devices can be scaled to larger networks and flexible systems with the help of AI
• In the near future, technologies like AIoT combined with 5G speeds can enable cloud gaming, where high-power PCs can be operated remotely for a good gaming experience from a low-power PC

The Future of AIoT and Its Challenges

When we consider how impactful AIoT will be for the future, we need to weigh in the downsides as well. 

For starters, the biggest problem with AIoT is compliance issues by manufacturers. Since there is so much data that goes back and forth, there is a huge incentive for companies to use that data for their benefit. Which goes against moral and ethical compliance regulations. For that, all companies are now required to follow privacy and data regulations like GDPR and CCPA.

Another concern comes from IoT security, where you need to address external threats from hackers and malicious parties. This can be solved by stronger security measures from manufacturers and basic steps that users need to follow for security. Like, stronger passwords, sophisticated authentication, safe browsing and usage, and use of security software like antivirus and firewalls.

Apart from that, AIoT systems are vastly complex and hard to implement. The supply chains, models, equipment, APIs, and all the components needed for it must be improved for higher levels of speed, accuracy, smooth operational ability, and accessibility. Just like 5G, newer and more innovative technology should take AIoT to a level that is more efficient and can be implemented in more ground-level applications. 

Despite these drawbacks, there is no doubt that this new version of IoT which is powered by AI and 5G, will be the go-to solution for most devices and functions associated with them. In fact, the implementation is already far begun. 

The post Artificial Intelligence of Things (AIoT)- The Merger of Two Modern Marvels appeared first on Tech Research Online.

Pretty much all designers and content creators want to bring more creativity to your posters and presentations. 

And, we wouldn’t want you to miss out on it or stay inside that generic pool of boring posters and presentations!

Presenting the new Canva AI.

Early Days of the Tool & Inspiration Behind Canva AI

The Australian company, Canva started just over a decade ago with the idea of “Empowering the world to Design”. Ever since its journey as a design platform began, the tool has been all about versatility in terms of formats, designs, templates, and much…much more.

You could create presentations, social media posts, videos, basic animations, and even basic PDFs. Thanks to this history of evolution, we knew Canva was going to venture into AI. 

But, we’re finally going to have a creative AI design tool that makes your designs much more accurate, creative, and innovative.

During Canva’s virtual create event, they announced a set of AI-Powered tools. Over 150 million users all over the world saw the live event and have been buzzing about the launch. 

Are you also one of them, wondering about these magic tools? 

New Canva AI Tools at Your Fingertips 

There are 5 new features incoming with the launch of Canva AI. Here’s all we know:

1. Canva Magic Design Tool

Source- Canva

Magic Design converts your thinking and ideas into templates of your choice instantly. 

So, it is an AI-powered design generation tool that aims to change any kind of media into more innovative and interesting templates. All you need to do is give it some context and content. 

It also helps in visualizing and refining your innovative ideas to live templates with the help of AI. 

At first, they suggest eight templates that best fit your ideas, but you can further edit and design on your own if you are not satisfied enough with the suggestions.

2. Magic Edit – Adding and Replacing Anything You Want

Source- Canva

Want to change the background design of your living room? Now, you can do it easily with this AI tool. 

Say, if you want the design of your living room to be filled with daffodils instead of jasmine, then also you can edit that. 

The main motive here, is to provide shape to your innovative ideas. And, with the Magic eraser, you can omit the part which is no longer needed. You can then replace it with whatever fits your ideas. 

Simply put, you can brush off the area that you want to edit and type what you want to insert. Based on that, it will give you suggestions. Accordingly, you can use or edit that based on your interest.

3. Magic Write – The AI Text Generator  

Source- Canva

Wondering about what if you want your document in your regional language? Magic write can make it simpler for you! It comes in 21 different languages. Not only that this time you can use it across the entire Canva suite not just limited to Canva Docs. 

It also helps you summarize your texts and fix grammatical errors you may have missed. You can also give a command about what you want it to do, and it will follow those instructions with the help of AI. 

The most interesting part is that this service is available to you across all Canva project types. Starting from presentations to social media posters, Magic Write will get it done.

4. Beat Sync – The Online Audio-Video Syncing AI Tool

Source- Canva

With the new generation getting more interested in short videos, Canva came up with Beat Sync. As the name suggests, it syncs music to beats for better audio quality. 

All you need to do is choose a music track of your choice. Then, click on the waveform to see the Beat sync on the menu bar. Once you see it, select whether you want to manually mark beats for free or automatically sync music to videos with the paid version.

5. Text to Image Generator

Source- Canva

Want to see a lion and a cockroach sitting at a coffee shop, having a one-to-one conversation? Text to Image generator will bring that to life, within seconds. 

All you need to worry about is visualizing and writing a prompt about what you want to see. After that, wait for the magic to happen. A somewhat accurate visual representation of what you wrote will be there in front of you on the screen. 

You can use this feature on all formats.

Is Canva AI Available on the Free Version? – Final Words

Just like the rest of the features, Canva offers different slightly rigid, more compact versions for the new Canva AI-based offerings. 

If you can flush out some cash for the paid version, I’d say it’s worth the money! To be precise, $12.99/month at the base level. 

Are you ready to give it a try? The New Canva AI features are surely on our calendar. You can stay tuned to see those posts on our Instagram @techresearchonline. 

Also Read: Top 10 Must-Have AI Tools For Business In 2023

The post Canva AI – Unleashing Creativity with AI-Powered Design Tools appeared first on Tech Research Online.

Every piece of tech content remains unfinished without the mention of artificial intelligence and machine learning. Today, the terms are equally hyped and are interchangeably used to explain an intelligent system or software. In fact, when we dive deeper into the broader branches of technology (like Big Data or Analytics), both terms frequently appear on the front face. As a result, most people use the terms synonymously—which leads to confusion.  

But, don’t worry! In this blog, we will cover the major differences between artificial intelligence and machine learning to eliminate this very confusion. However, before we proceed with learning the differences, let me help you grasp a broader understanding of what artificial intelligence and machine learning are.  

Artificial Intelligence 

To begin with, artificial intelligence is a computer’s ability to imitate or mimic human intelligent behavior and perform tasks the way humans do. Basically, it performs tasks that require human intelligence such as thinking, reasoning, applying logic, and essentially, making own decisions.  

“Artificial intelligence would be the ultimate version of Google. The ultimate search engine that would understand everything on the web. It would understand exactly what you wanted, and it would give you the right thing. We’re nowhere near doing that now. However, we can get incrementally closer to that, and that is basically what we work on.” —Larry Page  

In layman’s, the words ‘artificial’ and ‘intelligent’ combine to imply “a human-made thinking power.” Currently, AI is being incorporated into our day-to-day chores and in every sector. From finance to lifestyle, every sector has integrated artificial intelligence to streamline various processes. But, how did the useful branch of technology come into play?  

Timeline of Artificial Intelligence 

Source

# AI Then:  

Although AI has been around for several years, numerous people had begun exploring it in the 90s itself. Rockwell Anyoha’s 2017 paper on “The History of Artificial Intelligence,” which begins with the subhead ‘Can Machines Think?’, cites the Tin man from The Wizard of Oz as well as the young British polymath Alan Turing to enunciate the existence of AI. The paper further cites how it was Turing who explored the mathematical possibility of artificial intelligence.   

Turing’s paper published in the 1950s (Computing Machinery and Intelligence) discusses how to build intelligent machines and test their intelligence. Under this, he argues if humans use available information and reason to solve problems and make decisions, why can machines not do the same? 5 years later, Herbert Simon along with Allen Newell and John Shaw altogether created the first program written to emulate humans’ problem-solving skills— ‘Logic Theorist’.  

Furthermore, the term ‘artificial intelligence’ did not come into existence until McCarthy coined it in a proposal for a summer research conference. He turned the tides for AI through his proposal which read:  

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.” 

# AI Now: 

Fast forward to the 2000s and AI has already started to integrate into our daily lives. We visualized self-driving cars, personalized virtual assistants, robotic management, and many more when we envisioned the future. However, these aspects have been embraced in the present itself—making the future more enthralling! Although AI has been around for more than a few years, it has exponentially grown and has increased our dependency on it.  

As we have transcended to the evolution of AI, I would like to mark the words of Stephen Hawking (someone who requires no introduction),  

“The development of full artificial intelligence could spell the end of the human race. It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete and would be superseded.” 

# AI Likely in Future: 

While Hawking had subtly warned humans against the evolution of AI in the past, the present-day leaders are also advocating their arguments over the same. Speaking of AI’s evolution, we can not neglect to mention the popular tech billionaire, Elon Musk who, despite being paramount of his affinity for technology, especially AI, has said, “Mark my words—A.I. is far more dangerous than nukes.”  

The concerns over the increasing dependency on AI does not limit to tech enthusiasts and billionaires. Previously, several people have expressed their concerns against AI robots taking over humans in various fields of work as well as life.  

To summarize, the incorporation of AI has its own set of advantages as well as drawbacks. To better understand the technology, let us have a look at some of its examples.  

3 Common Examples of AI Incorporation 

Artificial Intelligence is commonly used in our everyday lives. Following are some of the notable instances of AI incorporation:  

1. Personalized AI Assistants

Alexa by Amazon, Siri by Apple, S Voice by Samsung, Cortana by Microsoft, and Google Assistant. All of these are perfect and most popular examples of personalized AI assistants. These tools have enabled human interactions with gadgets and have enabled us to do a plethora of things from hotel bookings to window shopping. 

2. Robotics 

AI robots are another example of AI integration. Think of the world’s first humanoid robot, Sofia, who is incorporated with artificial intelligence. Her creators claim that Sofia personifies their dreams for the future of AI. She imitates human gestures and facial expressions and is able to answer certain questions. Sofia can also initiate conversations on a variety of predefined topics. In fact, AI robots have a keen role to play in the future.  

3. Marketing 

AI has a great role to play in facilitating the future of marketing. With tools like Slack and Grammarly, today marketers are allocating huge amounts of financing towards incorporating AI in their marketing tactics.  

Now that we have learned about AI and its examples in a brief manner, let us move forward to understanding Machine Learning in depth.  

Machine Learning 

According to IBM, Machine Learning is 

“a branch of artificial intelligence and computer science that focuses on the use of data and algorithms to imitate the way humans learn, and gradually improves its accuracy.”

According to Wikipedia, Machine learning is  

“a field of inquiry devoted to understanding and building methods that learn, that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence.” 

 

In layman’s, Machine Learning or ML is the subset of AI with an ability to automatically learn from the data without explicitly being programmed or assisted by domain expertise. The ‘learning’ in ML refers to a machine’s ability to learn based on data as well as on an ML algorithm’s ability to train a model, evaluate its performance or accuracy and then make predictions. 

“A baby learns to crawl, walk and then run.  We are in the crawling stage when it comes to applying machine learning.” —Dave Waters 

 To simplify it further, Machine Learning is a current application of AI, that is based on the idea that we should be able to give machines access to data and let them learn from it for themselves.  

How did Machine Learning come into being? 

Source

 There are two important breakthroughs that led to the evolution of ML as the facilitating vehicle that is driving AI development forward with lightning speed.  

• • Firstly in 1959, Arthur Samuel realized that instead of teaching computers everything they need to know about the world and how to carry out tasks,  it is better for them to learn for themselves.  

• • Secondly, the emergence of the internet and the boom of digital information that is generated, stored and made available for analysis.  

When these innovations were in place, engineers realized that it would be efficient for computers. Machines to learn for themselves instead of being taught to. It would be wise to code them to think like humans. Then plug them into the internet for giving them access to all available information. Thus, began the era of MACHINE LEARNING.  

Let us now explore some classic examples of Machine Learning.  

3 Common Examples of Machine Learning 

Today, ML is relevant in many fields as well as industries and has the potential to further grow over time. For instance, you might be aware of image and speech recognition. These two are common real-world examples of ML.  

1. Image and Speech Recognition 

Image recognition is a widespread example of ML. It helps identify an object as a digital image, based on the intensity of the pixels in black and white images or color images. For example, labeling an x-ray, assigning a name to a photographed face, recognizing handwriting, and many more. ML is also used for facial recognition within an image in which using a database of people, the system identifies commonalities and matches them to faces.  

Moreover, ML can also be used to translate speech into text. Certain software apps are capable of converting live voice and recorded speech into a text file. Here, the speech can be segmented by intensities on time-frequency bands too.  

2. Medical Diagnosis 

In the past few years, Machine Learning has played a significant role in the diagnosis of diseases. Various physicians use chatbots with speech recognition capabilities to discern patterns in symptoms. Assisting in formulating a diagnosis or recommending treatment options requires the incorporation of ML. In fact, oncology and pathology also use machine learning to recognize cancerous tissues and analyze body fluids.  

3. Data Extraction 

ML helps extract structured information from unstructured data. Several organizations collect huge chunks of data from customers and using ML algorithm, they automate the process of annotating datasets for predictive analytics tools. Examples: Generating models to predict vocal cord disorders, developing methods for prevention, diagnosis and treatment of disorders, and many more.  

Since the data extraction process is tedious, ML simplifies it by tracking and extracting information to obtain huge volumes of data samples. 

How do AI and ML work to solve problems? 

Machine Learning and Deep Learning are Subfields of AI. Artificial Intelligence, as a whole, consists of various subfields, including neural networks, deep learning, computer vision and natural language. To understand how AI incorporates the various subsets of ML to solve problems and complexities. We have to first understand the meaning and processes involved in the below-listed terminologies.  

1. Neural Network 

Machine learning automates analytical model building by using methods from neural networks, statistics, operations research and physics to find hidden insights in data. It does so without being explicitly programmed where to look or what to conclude.  

So, a neural network is a kind of machine learning that is inspired by the functioning of the human brain. It is made of interconnected units (which look similar to neurons in a human body) and processes information by responding to external inputs, and relaying information between each unit. The entire process requires multiple passes at the data to find connections and derive meaning from undefined data.  

2. Deep Learning 

Deep Learning is one of the frequently used terms in the world of machine learning. So, what exactly is deep learning?  

The process uses huge neural networks with several layers of processing units. Deep Learning leverages advances in computing power and improved training techniques to learn complex patterns in large volumes of data. Being one of the most important parts of AI, Deep Learning has significantly contributed to the field. However, it requires huge amounts of data to extract useful inputs. Some of the common applications of deep learning are image and speech recognition.  

3. Computer Vision 

In the case of computer vision, they rely on pattern recognition. Deep learning to recognize all the elements in a picture or video. When machines can process, analyze and understand the images. They can better capture images or videos in real-time while interpreting their surroundings. 

4. Natural Language or NLP 

It is, basically, the ability of computers to analyze, understand and generate human language, including speech. Its next stage is natural language interaction—a process that allows humans to communicate with computers using normal and regular language to perform tasks. Although machine learning is all about the idea that machines should be able to learn. And adapt through experience, AI, however, concerns a broader idea where machines can smartly execute tasks. 

In the end, AI applies machine learning, deep learning and other techniques to solve actual problems.  

Why do people often confuse Artificial Intelligence and Machine Learning?  

(This section requires your complete attention). Although machine learning is a subset of artificial intelligence, there are a few basic differences between both aspects of technology. We explored (in brief) the definitions and common examples of AI and ML. Till now, you would have understood how these terms are co-related and what their actual work involves.   

Considerably, ML is a subset of AI. As both terms are interchangeably used, and hence, people confuse them to be synonymous. However, both terms are different from each other in various ways. While AI implies the general ability of computers to imitate human thoughts and perform tasks in real-world environments, ML refers to the technologies and algorithms that enable systems to identify patterns, make decisions and improve themselves through experience and data. Moreover, machine learning and deep learning are subfields of AI.  

To further clarify the differences, I have put together a list of factors/features that differentiates AI from ML in the below table.  

Difference Between Artificial Intelligence and Machine Learning 

To put into context, “All machine learning is AI, but not all AI is machine learning.” Below is a table enlisted with the major differences between artificial intelligence and machine learning.  

 

“Everything that moves will be autonomous someday, whether partially or fully. Breakthroughs in AI have made all kinds of robots possible, and we are working with companies around the world to build these amazing machines.” —Jensen Huang, Nvidia CEO 

 

The listed aspects are some of the common differences between artificial intelligence and machine learning. Essentially, artificial intelligence is a broader family consisting of machine learning and deep learning as its components, whereas machine learning is a subset of artificial intelligence.  

Capabilities of AI and Machine Learning in Business 

Till now, we have comprehensively learned about artificial intelligence and machine learning in detail. You might have gained a thorough idea of what these technologies are, how exactly they work and how they’re different from each other. You also might have concluded that AI and ML are some of the necessary factors to be successful in any industry. Speaking of success, organizations must be able to transform their data into actionable insight. And this advantage of automating a plethora of manual processes (that involve data and decision making) is provided by AI and ML.  

In a nutshell, incorporating AI and ML into systems and strategic plans allows leaders and the management to better understand and act on data-driven insights with greater speed and efficiency.  

For Machine Learning: 

ML is already pivoting various applications that you use every day.  

• • For example, Meta (formerly Facebook) uses ML to personalize the news feed of users. This is why you keep receiving similar posts or posts by those creators whose content you have previously liked. (In simple words, if you have liked various posts of Kim Kardashian, your feed will be populated by more posts by Kim K.) 

• • Did you know that your GPS navigation service also uses machine learning to analyze traffic data and predict high-congestion areas on your commute?  

• • Even your email spam filter is using machine learning when it routes unwanted messages away from your inbox! 

Apart from its integration in our daily lives, ML has a great role to play in the enterprises as well.  

• • It can help pull insights from large amounts of customer data. So that companies can deliver personalized services and targeted products based on individual needs.  

• • In the case of regulated industries like healthcare and financial services, ML helps strengthen security. Compliance by analysing activity records to identify suspicious behaviour, uncover fraud and improve risk management.  

• • Generally, ML and other AI techniques can provide an organization with greater real-time transparency so the company can make better decisions. 

For Artificial Intelligence: 

Companies integrate AI into various areas of their operations. From customer services to sales and marketing, AI plays a vivid role in helping companies succeed. Let us have a look at how AI is helping companies and enterprises:  

• • For customer services, AI is used for answering customer questions via AI-powered chatbots, improving credit card fraud detection, analyzing customer feedback and surveys, and many more.  

• • For sales and marketing, AI helps create accurate forecasts by studying historical and market data, updating customer contact information, generating new leads and optimizing lead scoring, and many more. In fact, companies use AI to create personalized messages as well as curated content streams. And digital ad programs that deliver offers customers want, and optimize pricing in real-time based on competitive and market factors. 

Opinion: What can we expect from Artificial Intelligence and Machine Learning? 

(You’ve finally reached the end of the blog. So, congratulations!) Artificial Intelligence and Machine Learning are already blooming now. In fact, numerous companies are investing billions of dollars in AI and ML. While there are several things that AI and ML can do to accentuate humans. But there are many things that they cannot do. There are certain limitations to these technologies.  

50 years down the lane, when historians decide to go through the book of (crazy) advances in the 2020s. They will analyze how impactful AI and ML have been for the future of the world in general. Today, we are building machines that can mimic humans and their language, creativity as well as their thoughts. And what would that mean for the future? Consequently, AI and ML will only propel the future of all industries and sectors. By now, the hype of these technologies has exceeded the likes of reality. The advances in various important areas have become equal and even surpassed the capabilities of humans.  

So, if you have not paid attention to artificial intelligence and machine learning yet, it is high time that you should.  

‘Also Asked’ for Artificial Intelligence and Machine Learning 

#  What is the main difference between artificial intelligence and machine learning?  

While AI is a technology that enables machines to imitate human behavior. The ML is a subset of AI that allows machines to automatically learn from past data without programming explicitly. In short, the goal of AI is to build a smart computer system, comprising human intelligence, to solve complex problems. 

# Who is the father of AI? 

John McCarthy is known as the father of artificial intelligence.  

# Which language is frequently used for AI programming? 

Python is widely used for artificial intelligence. It comes with packages for several applications including General AI, Machine Learning, Natural Language Processing, and Neural Networks. 

# Who invented Machine Learning?  

Arthur Samuel (1901-1990), an American pioneer in the field of computer gaming and artificial intelligence, coined the term “machine learning” in 1959. He defined it as a “field of study that gives computers the ability to learn without being explicitly programmed”. 

# What is the main difference between Machine Learning and Deep Learning? 

Machine learning is about computers being able to think and act with less human intervention. Deep learning is about computers learning to think using structures modeled on the human brain. 

Anwesha Mishra

Anwesha has been a creative writer for a while. Currently, on her pursuit of tech writing, she is diving into the realms of technology to produce better content on the forever-changing world of technology. In her free time, you’ll find her humming tunes of her favourite shows or reading a book.

The post Artificial Intelligence and Machine Learning: What do we know so far?  appeared first on Tech Research Online.

Artificial Intelligence is at the heart of a seismic shift in the financial industry. It has already started to empower banking organizations to redefine operation, establish innovative products and services, and impact customer experience.   

And the best part: we’re just beginning to feel the tremors of a far-reaching revolution.   

We are entering the machine age, banks who are early adopters of these technologies will have a serious advantage and find themselves on the competitive edge.

However, if they lag then the upstart fintech firms will leverage these advanced technologies to take them over with their sophisticated algorithms.   

Hence, if the players in the finance industry want to maintain a sharp competitive edge, they need to embrace AI and carefully weave it into their business strategy.  

In this blog post, we will examine the dynamics of AI ecosystems in the banking industry by understanding AI’s colossal impact on banking.

We will also see major disruptions in the industry. Finally, we’ll explore how AI is changing banking and its future financial impact.   

1. Financial Institutions Became early Adopters of AI to Secure their Futures. 

Analysts predict that throughout the next 10 to 15 years, AI applications will create $1 trillion funds for the financial industry in savings. These savings will be achieved through a mix of office efficiencies encompassing everything from improved data processing to shifts in staffing levels.   

One trillion dollars is a huge number; however, it fails to help us understand the impact these applications will have on midsize FIs. But, to translate it more easily consider these figures for your bank:    

• 34% increase in revenue  
• 22% reduction in operating expenses  
• 30% higher sales conversion rates  

Now, imagine the impact of those results on your bottom line!   

AI is poised to spur unprecedented gains for all those who are prepared to embrace it in the financial industry. More than 70% of big banks are already planning to implement AI solutions for front- or back-office.    

Unfortunately, midsize banks are struggling as only 2% have deployed technology and in the near future, only 13% are planning to invest in AI.  

For sure, it’s a challenge, however to midsize banks need to do some forward-thinking and their credit unions should recognize it as an opportunity if they want to flourish in the future. Most importantly if competitors are ignoring it then it means that it is time to begin implementation.   

2. AI will Fuel Revenue Growth 

By the year 2030, artificial intelligence-powered applications will boost revenues by 34%.   

How?   

AI-powered applications can help boost revenues by leveraging the power of machine learning. Deep learning applications can identify motivations and sales triggers by scanning millions of records and examining consumer behavior.   

Then, computers can be used to automatically deliver targeted messages to customers by applying that knowledge.  

3. Higher Conversions with Personalized Offers 

The foundation of sound marketing practice is delivering the right message (offer), to the right people, at the right time.   

New-age bankers draw on experience to achieve that trifecta and drive customers to their branches. However, with AI, they can take a deeper dive by automatically delivering personalized offers which makes it more likely for customers to act on.   

The best part, you don’t need any staff intervention. For instance, organizations that implement virtual assistants and chatbots for customer service report 30% higher conversion rates from sales.  

4. Automated up and Cross-Selling 

Artificial intelligence in the banking sector can learn consumer behavior trends and according to that auto-suggest up and cross-sells to interested customers. The technology can suggest appropriate selling to bank staff during their face-to-face interactions with customers.    

Let’s take an example, most of the current web interfaces have post banner ads and pop-ups to automate upsells and cross-sells. However, we are not sure as to how many times they work or if they are as efficient as they could be.    

Instead, what if, you have a chatbot to greet customers by their name and voice? What if that chatbot or assistant initiated conversations based on a user’s transaction history? For instance: “Hello Ann, I can see that you sent nine international wires in the past week. Did you know of other electronic payment options available at a lesser cost?”  

Unsurprisingly, customers are more responsive to that kind of prompt than banner ads.   

5. Robo-Advisors 

Are we talking about taking financial advice from a machine?   

Yes, and trust me it’s not that far-fetched. Plus, it can yield far bigger dividends than personalized advice as that is prone to human error.   

In reality, customers can process little AI and data that funnels through numerous neural network layers. Plus, its solid market advice helps keep customers coming back by building wealth.    

6. Alerts for High-Risk Customers 

Artificial intelligence applications can help banks recognize warning signs that a customer is about to jump ship.   

How?  

Well, AI can do this simply through constant monitoring and tracking their reduced platform login frequency and large withdrawals, for example. Computers can, then, automatically alert banking staff, giving them a chance to intervene.    

Such automated processes help grow revenues by freeing banking staff and saving time to focus on deeper, valuable customer engagements. In turn, that can help yield greater profitability by improving a better customer experience, and ultimately, earn more sales.   

7. AI will Offer Significant Savings 

Increased revenues are just a part of the equation as AI implementation can help banks save tons of money. This is because there’s no better way to cut costs than with artificially intelligent applications without jeopardizing the quality of service.   

In fact, artificial intelligence in the banking system can deliver a better customer experience as it allows staff to focus on customer retention.    

8. Enhanced Customer Experience 

Artificial intelligence-powered chatbots and virtual assistants in the banking system are a breakthrough. They can onboard new customers, answer customer questions, and help in customer account management.   

This means that banks will no longer need staff to move money between accounts, or help customers reset their passwords, or find months-old bank statement copies.    

Moreover, image recognition can eliminate the need for passwords through advanced facial and biometrics recognition. This will enhance the customer experience, save time, and reduce costly security breaches.   

Banks can also leverage Natural Language Processing (NLP) for direct interactions with customers via virtual assistants such as Siri and Alexa. These bots can be deployed on different platforms, such as Facebook Messenger, to reach customers in their comfortable environment.   

Indeed, by implementing AI for customer service, organizations report 33% savings compared to a live agent call, 70% fewer calls and email inquiries, and massive savings in staff time.   

9. Improved Operational Efficiency  

Experts say banks that implement AI report a 22% reduction in operating expenses as compared to those savings through saved staff hours and error elimination.   

10. Accurate Processing  

Today, 70% of the banks prioritize integrated receivables, and for good reason For instance, according to some NACHA estimates more than 60% of ACH payments arrive separately from remittance information.

Stranded receivable means that staff members have to track down email remittances and manually enter data. This, in turn, delays posting, lengthens DSO, and impacts cash flow.   

By leveraging intelligent automation, banks can analyze large unstructured data without human intervention and reassociate payments. In fact, AI can increase processing rates by up to 95%.   

11. Workflow Automation, Contract Reviews, and Reporting 

Bank staff analyzes and organizes unstructured data which is tedious, costly, and error-prone work. The banking sector can utilize artificial intelligence algorithms and robotic processes for quick automated workflows and eliminate the need for human involvement.   

Over time, AI become even more efficient and lead to billions of dollars in saving across the financial industry.    

12. Improved Risk Management and Compliance 

We all know that fraud costs banks millions if not billions. And, even if a bank is lucky enough to reclaim funds lost through the fraudulent transaction, they have to relegate staff to fraud management.   

The application of artificial intelligence in the banking system can help prevent fraud. AI algorithms can scan millions of credit card transactions to detect potentially fraudulent transactions.    

Moreover, AI-powered applications can help banks automate Anti-Money Laundering (AML) and Know Your Customer (KYC) compliance. These tools can extract data from a different source to quickly flag suspicious activity during onboarding or examine millions of transactions.    

Finally, AI supports reliable credit decision-making by analyzing millions of data points against both traditional and non-traditional criteria to arrive at instant credit decisions. For instance, borrower education and job history.

This will be beneficial to financial institutions at three-folds: minimize risks, confident investment in high-value customers, and quickly lending of funds to avoid losing business to competitors. Lastly, this reduces the need for human intervention.  

Author Bio:

The post AI In Banking: How will Artificial Intelligence Change the Banking Industry?   appeared first on Tech Research Online.

We live in a world of modern technology, and even though people have been saying for many years that their modern time is the best one. It is evident that there is no complete truth to that since technology improves all the time. And it should evolve. 

One thing that became extremely popular in the last couple of years, or at least people talk about even not knowing completely everything about it, is Artificial Intelligence.

AI has enormous potential and will be part of our future for many decades to come. It helps in a number of things, from picture recognition, data processing, advanced web search engines, and understanding human speech. You have talked to Siri, right? – it is a bit strange but exciting. Also, self-driving cars like Tesla or playing strategic games are on the menu. 

Artificial Intelligence is made by AI developers that can build AI functionality in software apps. And this is the main reason why we must be thankful to companies like Adeva. That provide the best hiring global talent in software engineering that make our lives so much easier.

There is a lot to learn in order to understand AI, but what is essential to all of us is that it provides valuable advantages. Not everything has to be extremely intelligent or have high-tech purposes. AI also has a fun and engaging side, and here come the funny AI memes.

That is why here we will see the top 10 funny AI memes that are funnier than those created by humans. But before we get into it and see the funniest AI memes, first, let’s see how AI memes became a thing. 

People that create memes should be concerned about how AI creates funny memes. Because it can easily outdo them, and this fact is impressive by itself.

The website ImgFlip allows users to make memes, but it also decided to have its AI-Based Meme Generator titled “This Meme Does Not Exist,” which uses machine learning.

It was launched in 2019, and there is no way you haven’t seen at least one meme on the internet. Since it has some of the most popular ones with Drake, Sean Bean from GOT and Lord of the Rings, Facebook memes, Leonardo Di Caprio memes from the Gatsby movie, and many others.

There are also other sites where you can find AI-generated memes. Some of them are TweetMakers or InspiRobot, and many more you can easily find online. The only problem is that some are used to harass famous people online or spread fake news.

And this is an issue that will be dealt with probably in the future. But on the other hand, there are many funny and interesting memes. so let’s see the top ten best and most entertaining AI memes you can find online:

#1. Mark Zuckerberg AI Memes

There are many memes made with the face of the Chief Executive Officer of Facebook, Mark Zuckerberg, but one that is extremely popular and funny is this one that indicates that the privacy policies of Facebook aren’t as private as we think they are.

#2. Drake Memes

Drake is immensely popular for his music, but maybe even more popular because of all the memes made with him from the “Hotline Bling” music video.

This is one of the most famous online Drake memes describing the funny moment when you are not sure of liking a girl, but you “fall in love” with her dad.

#3. Jonathan Goldsmith’s “I Don’t Always”

The Most Interesting Man in the World was an advertising campaign made by the beer-making company Dos Equis.

They decided to hire American actor Jonathan Goldsmith for the marketing campaign and made commercials with the famous line “I don’t always drink beer, but when I do, I prefer Dos Equis.”

Well, Jonathan probably wasn’t aware at all that this line would become even more popular because of its enormous usage for making memes online.

These memes are funny and honest in an authentic way, and they all start with “I don’t always“. Here is one that millions of people love to have a laugh about.

#4. Sean Bean Lord of the Rings Meme

Sean Bean is one of the greatest actors ever. He definitely became one of the funniest meme creating examples. Since he appeared on memes from a LOTR scene when he explains something.

Here is a Sean Bean meme from one of those “One Does Not Simply“. That became immensely popular with the online community. 

#5. Yoda AI Memes

Yoda is maybe the most popular fictional character from the “Star Wars” universe movies. It is the cutest creature you will ever see, but it is even more fun to see the Yoda AI memes. This one like where it indicates Yoda says the famous Darth Vader line “The force is strong with this one.”

#6. Distracted Boyfriend

One of the most popular memes is the “Distracted Boyfriend” meme. Where a guy who walks with his girlfriend holding hands looks at another girl. Of course, the high-level creativity makes all sorts of memes out of this, one of the best being exactly about memes.

#7. Batman Slapping Robin

Batman and Robin are known for their collaboration in trying to save the world, but the meme world made a lot of fun of it in a different way.

Some of the best AI memes are with the image of Batman slapping Robin interrupting him for something he says.

#8. Computer Animated AI Memes

As you already saw from the previous examples, memes can be created out of anything. Some of them with the best honest humor are AI animated memes.

To give you an example, here is one that represents something that we all have felt at least once in our lives.

#9. Laurence Fishburne as Morpheus

Many of us barely wait for the latest sequel of “Matrix” to come out. And the only thing that saddens us is that the legendary character Morpheus won’t be play by Laurence Fishburne.

But Laurence is known for playing Morpheus great and with beautiful charisma, and also known for great memes made with the famous line “What if I told you.”

Surprised or not, he actually doesn’t say this line in the movie. But it became iconic with AI memes like this one here. Most of them are funny, and some can be motivating too. 

#10. Di Caprio as Gatsby

Last but not least, the legendary actor Leonardo Di Caprio’s characters also became some of the most used images from movies to create a meme.

From “Django Unchained” and “Inception” to “The Great Gatsby,” there are many AI memes created. Here is one to end this exciting list with Di Caprio as Gatsby, making a toast. 

Conclusion

These are the top ten most exciting and funniest online AI memes. It is obvious that Artificial Intelligence is improving all the time, and AI memes are part of this interesting path.

Memes have always been funny, but AI memes are even more engaging. Since it is amazing how a ‘the computer’ can invent humor and especially one that makes humans laugh. 

It sounds a little bit scary, but on the other hand, it is a fact that Artificial Intelligence is the future, and nobody can deny the continuous development of this technology. 

Author bio:

Metodi Rizov is a content writer who is enthusiastic about machine learning, AI, and all tech-related things in the fast-spinning world we live in.

The post Top 10 Funny AI Memes (Created By Humans) appeared first on Tech Research Online.

In the ultra-modern world, facial recognition technology has emerged and is becoming a vital part of cyber security. Facial recognition is one of the mainly used biometric technologies in the corporate sector.

Diving into the example of social media, when the user uploads their photo and such platforms automatedly tag the different persons in the photo which might give the thought of the technology how it’s making convenience to do so.

However, Artificial intelligence-based facial biometric verification software is capable of tracking down persons’ faces without their consent which raises the question regarding privacy invasion.

Like other technology advancements, facial recognition also brought positive and negative opportunities.

The usage of a facial biometric system is rapidly increasing, so it would be helpful to be well aware of the pros and cons of facial recognition.

1. Facial Recognition – Brief Overview

The concept of face verification has always lived in the fantasy world. Ranging from any kind of tool to solving complex crimes. Nowadays biometric technology has evolved to the extent where we can see around us irrespective of the fantasy world.

Quoting the example of Apple’s Face ID technology, the biometric is swiftly becoming more real as compared to the fictional concept.

Facial recognition is considered as the type of biometric technology which enables the systems to determine and verify the person’s identity through digital photos by mapping the facial features and traits mathematically.

Facial verification is stated as the fastest and accurate biometric technology to perform human facial identification. 

• Capture: In the first stage, the sample of physical and behavioral conditions are collected during the prescribed time
• Extraction: the templates are developed by extracting the information the samples captured earlier 
• Comparison: once the data extraction is done, the information is compared with the existing templates
• Matching: in the last step of the facial recognition process the decision is to be made which is truly based on matching the obtained facial templates with the facial imprints placed in the database 

2. Pros of Facial Recognition 

Online face verification is advertised as the technology which makes lives more easy and convenient. This solution eliminates the need for memorizing passwords or pins to verify the identity.

#Finding Missing People and Determine Preceptors:

Government and law enforcing agencies have widely been using face biometric technology to identify the criminals which tend to be ghosting whereas there is no other way of determining such people.

On another hand, the lost peoples are also easily identified by comparing their facial imprints using live camera feeds against the global watchlists. This technology is also aiding in finding the children that are victims of human trafficking.

Artificial intelligence-based facial recognition technology is embedded into the aging software which draws the image of kids, portraying how the children would be looking in coming years, and by comparing with the present image police are able to determine such potential matches.

#Boosting Security in Banks and Airports:

The facial biometric is a vast spread in the domain of finance and travel. It is the source of improving the safety as well as the security measures in retail sectors such as airports and banks. Customer screening is an integral part of airport security. 

This works the same as identifying the criminals from public places, the biometric technology is helping out to determine the customers which pose the potential threats which can impact airlines as well as the passenger. In addition, it’s quite beneficial for security agencies to verify the individuals requesting to cross the border.

Financial institutes like banks are also using biometric technology to enhance the security checks to prevent frauds and to determine the customer who was charged before for any of the potential crimes.

#Enhanced Health Treatment:

Biometric technology is also aiding healthcare, surprisingly it’s mainly used in the detection of genetic disorders. By examining the facial traitor of the patient, a biometric recognition solution can identify the genetic mutation pattern which is causing the syndrome. Biometric technology is faster and less expensive is used as compared to the traditional test.

3. Cons of Facial Recognition 

#Threats to Individual and Societal Privacy:

The biggest downside of the facial biometric is the significant threat to privacy. People discourage the idea of taking photos and storing them by an anonymous group which can be used in the future.

Privacy is considered the biggest issue in the cities like San Francisco, California, etc where law-abiding regulators have banned the usage of surveillance systems embedded with facial recognition technology.

In suspicious cases, only law enforcement can record the videos or take photos from government allotted security video devices, but they lack the feature of face verification.

#Creates Data Vulnerabilities:

The facial imprints and videos require bulk storage, due to which the security concerns tend to rise as the databases are quite vulnerable and can easily be breached.

A large number of cases are reported regarding the database breaches in which the valuable facial scans were collected to use in finance corporations or police departments.

4. Features Of Facial Recognition Technology

The facial biometric verification mechanism has various features to ensure security and prevent impersonators from causing any trouble to the system. Following are some of the common features that facial recognition software provides: 

#Liveness Detection Analysis:

During the process of verification, liveness detection checks prevent any chances of spoof attacks by ensuring the live presence of the user. Fraudsters tend to deceive biometric verification checks as well but liveness detection analysis enables you to identify and eliminate such attempts beforehand.

#3D Depth-Sensing Analysis:

These checks are crucial to ensure that the image is not tempered with the ID document. It scans the different points on the image and compares them against previously saved digitized images. 3D facial recognition, AI, and human intelligence are utilized collectively for this check.

#Deep Fake Detection:

The facial recognition system cross-checks the user’s photo with the picture on his ID document submitted at the time of verification.

It uses AI mapping techniques, image distortion analysis, and microexpression checks to detect any kind of deep fakes and 3D masks spoofing measures.

In case of a suspicious attempt, the system declines the verification status of the user and eliminates the impersonation efforts.

Conclusion:

The facial biometric systems with multi-factor authentication are effective countermeasures for finding missing people, increasing security surveillance at the airport, or cross-border checks. It provides an additional layer of security to deter the increasing number of identity thefts.

Robust 3D facial recognition techniques such as AI mapping, liveness detection, and microexpression analysis detect and eradicate spoofing attempts made through deep fakes, photoshopped or distorted images, and 3D masks. There are numerous benefits of facial recognition technology.

Developed in the 1960s, facial recognition has become more accurate and advanced with time by employing several artificial intelligence algorithms. Facial recognition is one of the best biometric systems that identify the user by capturing his live selfie and matching it against other images in the database. It can address more sophisticated security challenges with unparalleled efficacy, sensitivity, and perception.

Author Bio:

Sophia Clark is a technical content/contributor writer and has a keen interest in writing insightful and high-quality content regarding the emerging trends in the domain of artificial intelligence, blockchain, machine learning, and data science.

The post Pros and Cons of Facial Recognition – Everything You Need to Know appeared first on Tech Research Online.

“The development of full artificial intelligence could spell the end of the human race….It would take off on its own, and re-design itself at an ever-increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.” 
— Stephen Hawking told the BBC

The genius of the world made a strong statement against the most sought-after technology “Artificial Intelligence.” There is no industry that it has not touched or no domain that it hasn’t helped perform better, still the greatest scientist of all times is sure of its self-destruction capabilities. When talking about the recent changes that the world has witnessed with the latest developments in the field of Artificial Intelligence self-destruction seems a faraway thing.  

Artificial Intelligence Solutions in the business arena has played some really long shorts that have made the biggest businessmen invest in it.  

As human intelligence is for now in the self-destruction mode with the outbreak of the pandemic the technology has opened doors to new alternatives.  

AI in several guises has made it evident that it will become important as businesses seek to identify, analyze and automate their functioning affected in the Covid times. Ai and ML development companies have made it easier to catch up with the changing times. 

Top 10 Artificial Intelligence Solutions in 2022 

The shift in the implementation of the technology in the IT sector has shown some dramatic adjustments in 2020 and the same continues further for 2022. As the pandemic broke down and the world was asked to work from home overnight things got a little messed up. Gradually, the technology extended its hand and helped organizations keep their functioning sane and improve their client handling process and in 2021 AI leveraged its power and various ways for strong results.  

1. Artificial Intelligence to Be a Major Part of Your Admin  

Talent acquisition during the pandemic became quite complicated and the onboarding process was quite messed up. So, enterprises adopted AI talent acquisition software that would filter and list the right set of potential employees without much human intervention.  

2. Artificial Intelligence Introduced us to Self Directed IT Firms  

As of now, we aren’t sure what changes that the work culture brought are here to stay forever and what changes would be defied once we adapt to the new normal. There are a lot of problems that can now be resolved by installing a few software. And to meet the advancement standards there are solutions that come with the capabilities of self-correction and self-improvement when it comes to smaller issues. They can report malfunction proactively and reduce the downtime of any unit of the business.  

3. Structure Unstructured Data with Artificial Intelligence 

Hey Google,  

Please Call Tom! 

And the call connects. With Natural Language Processing facilitating the working of unstructured data, it becomes quite easier for people to fix their images and email inboxes without much effort.  

Thus, they can arrange their work-related emails and others without putting in the effort, and working from home becomes quite easier. Also, to create data most organizations are now looking forward to robotic process automation as an alternative.  

Although the process cannot be fully automated, it becomes easier for people to make basic segregations and arrange their data.  

4. Accept Artificial Intelligence as Business Partner 

Over the years it has become quite interesting for artificial intelligence to walk the path with businesses. Although there is no limitation to the implementation of the technology with the businesses we still have walked a long path and would be covering better distances.  

The scope of technology has increased from just biometric attendances to automated tasks and interesting work solutions. 

One of the most noted features of AI and ML solutions is one can get ROI in real-time and look for better alternatives to earn better profits.  

5. Artificial Intelligence Turns Explainable  

With a restricted time domain, it becomes easier for people to accomplish more tasks. For years AI has been seen as a threat to human employment but in the times when paying decent wages became difficult, it was easier to look towards the technology as a reasonable choice to earn profits.  

Do you think it would have been easier for the companies to bear the financial loss and human resource loss at the same time??? 

6. Businesses Can Speak and Listen  

With voice and language-driven intelligence, it became quite easier for businesses to connect with the audiences. With technologies like Automated Speech Recognition, it became a blow for businesses to create content that was voice search-friendly.  

Businesses revamped their functioning, they took some interesting steps to reach their audience, and although the world struggling with the old and the new normal we walked a path that was engaging and revenue-generating.  

7. Artificial Intelligence Mutually Collaborates with Cloud  

As cloud computing was assisting enterprise mobility to its best, we now have AI to assist it. The existence of two of the most complicated technologies in the same environment was a slow process but the black swan of events accelerated their mutual existence in a go. In a span of a few months, the organizations were looking for solutions that meant working with both technologies easily.  

8. Artificial Intelligence Learns Human Ethics  

If you are looking forward to AI as your human resource replacement then surely you are having the wrong idea. If you want to have a solution that can help you earn better employees in terms of technology, then you need to wait. Artificial Intelligence would gradually be ready to take up odd jobs ethically by the end of the year.  

Till then keep an eye on your new assistance! 

9. Artificial Intelligence Can Analyze Your Pictures  

Woah! 

Just image search and you are done.  

With images and voice search being the best part of the business establishments, molding your business and technology to complement that was a little difficult. Instead of entering the product or the service they just drop a similar image and everything related to that image is on the screen.  

Your audience can check the products you want to offer that match their standards in a click and the purchase would be an easier process.  

10. Operating Systems for Artificial Intelligence  

Artificial Intelligence Solutions fosters the complexity of Information Technology with its new standards. Businesses see a whole new domain and functioning setup that is less flawed and highly productive. With limited human intervention, it becomes easier for businesses to complete the supply chain without any errors. Improvement in key processes, tasks, and decision-making through improvised solutions and analyzed data.  

 Conclusion:

These Artificial Intelligence Solutions has made it easier for businesses to settle up with the most reliable and credible solutions. Hire AI and ML experts in Singapore to upscale your business to match the current scenario. It makes it easier for businesses to connect with their employees, clients, stakeholders, and others to run a solution that is interesting.  

Since the outbreak of the pandemic has revolutionized the working of the enterprises in all the unseen ways, it is vital for your businesses to adapt to new changes.  

Author Bio: 

Keith Laurance is a technical content writer who has been working with the mobile app development team at Octal Info Solution. Over the years she has researched JavaScript app development and promises to deliver the most reliable solutions. Other than researching tech-related queries, she loves to eat and read books. You can always find her in the nearby market buying quirky elements for her super cozy place. 

The post Top 10 Artificial Intelligence Solutions in 2022 You Can’t Miss appeared first on Tech Research Online.

In 2020, despite the COVID-19 pandemic, we saw thousands of startups managing to produce major tech breakthroughs.    

 AI Startups all over the world manage to develop some innovative solutions. These innovations spanned from improving customer servicing to bringing more efficiency to healthcare.    

In this blog post, we have compiled a list of 14 emerging artificial intelligence startups that you should watch out for in 2022.   

1. Reekon    

Website- https://reekon.net/   

Domain- Customer Service   

Reekon is a semi-autonomous customer service automation platform powered by artificial intelligence. It was founded in 2020, the startup aims to help companies handle their e-commerce and IT customer inquiries.    

This platform enables organizations to generate answers and resolve customer queries automatically. This process leverages historical customer inquiries about products and services to generate actionable insights.    

Besides, Reekon’s platforms successfully integrate with multiple customer service software such as Fresh Desk, Zendesk, and WooCommerce to automate customer requests.   

2. Overwrite    

Website- https://www.siloam.co/   

Domain- Learning Platforms   

Overwrite, a Singaporean startup, provides an adaptive learning platform. This platform customizes the learning progression of users with the help of machine learning (ML). They aim to enable children to learn and grow irrespective of their socio-economic circumstances.   

The startup takes personalization a step further by creating the learning content as per individuals’ strengths and weaknesses. Lastly, it offers detailed analytics on the progress of users.   

3. WhiteBox HR    

Website- https://www.whiteboxhr.com/   

Domain- Human Resources (HR)   

WhiteBox HR, a UAE-based startup, develops ML algorithms for talent acquisition and management. Despite progress in terms of gender equality, there is still a long way to go. The startup aims to tackle gender inequality by eliminating recruitment bias in companies and help them focus on gender diversity while hiring.   

The startup utilizes AI and analytics for the augmentation of the employee experience which increases employee satisfaction. Other than that, it also helps boost engagement, positivity, and productivity for stakeholders.    

4. Accrad    

Website- http://accrad.com/   

Domain- Healthcare Diagnostics   

Accrad, a South African startup, develops a deep learning-based X-ray solution for radiologists, CheXRad. The solution analyzes X-ray scans against healthy scans to predict disease markers.    

CheXRad can drastically reduce the time taken by radiologists to analyze the scans and improves the accuracy. Moreover, the solution is trained with high-quality training set on a large scale.   

The medical imaging advances produced by CheXRad enable doctors to efficiently identify disease markers and predict potential health concerns. AI plays a vital role in enabling clinicians to focus more on the patient rather than reports.   

5. teX.ai    

Website- https://www.tex-ai.com/   

Domain- Text Analytics   

TeX.ai, a US-based startup, develops AI-enabled software for text extraction, classification, and summarization. The startup utilizes AI, NLP, and deep learning to provide actionable insights by cleaning unstructured data based on structured data.    

The startup uses AI algorithms to analyze word combinations from multiple sources of data. They then use it to identify opportunities for cost or process optimization.   

The resultant solution is multi-lingual and can process a variety of text from different formats such as reports, emails, text messages, calls, and others.   

6. Delta AI    

Website- https://deltalabs.ai/   

Domain– Social Media Intelligence   

Delta AI, an Australian startup, leverages the power of AI to distinguish content between reality and deep fakes. It leverages computer vision to develop social media intelligence tools to help realize the potential of happy customers.   

Through their platform, they analyze social media content and then reveal parts of the video that are invisible to traditional, text-based search. This solution aims to enable brands to make strategic marketing decisions with accuracy.    

Delta’s AI models interpret live data to provide contextual insights which can give a clearer picture of how products are being used around the world.  

7. Anodot   

Website: https://www.anodot.com/   

Domain- digital transformation industries   

Founded in 2014, Anodot, a United States startup, is an analytics platform. It leverages artificial intelligence and machine learning techniques to constantly analyze every business parameter. It provides real-time alerts and forecasts using unstructured log data and structured metrics data.   

Anodot was also named in the Forbes’ Top 25 Machine Learning Startups to Watch in 2020. Besides, it has a customer base of more than 100 companies in the digital transformation industries.   

8. Dataiku   

Website- https://www.dataiku.com/   

Domain- Data Analyses Industry   

Founded in 2013, Dataiku is an AI and Machine learning startup, headquartered in France. In 2014, the company announced its Data science studio. It is a ‘predictive modeling’ software for business applications.    

The company aims to bring engineers, data analysts, and scientists together to create self-service analytics and operationalize ML. Dataiku already has some of the big enterprises under its clientele including Unilever, General Electric, and Comcast.   

Alphabet Inc, Google’s parent company, also joined the company as an investor in 2019, achieving unicorn status. It is named a Leader in the Gartner 2020 Magic Quadrant for Data Science and Machine-Learning Platforms.   

9. Eightfold.ai   

Website- https://eightfold.ai/   

Domain- Human Resources (HR)   

Founded in 2016, Eightfold.ai, headquartered in California, is an expert in deep learning with the mission “Right career for everyone in the world”.    

The company’s talent intelligence platform manages the whole talent lifecycle for enterprises. It uses AI in the most effective way to retain top performers, upskilled and reskilled workforce, recruit top talent efficiently, and reach diversity goals for organizations.   

The USA-based AI Startups has also been named in Forbes’ Top 25 Machine Learning Startups to Watch in 2020.   

10. Frame.ai   

Website: https://frame.ai/   

Domain: Communication Industry   

Founded in 2016, Frame AI is a startup that has developed a collaborative messaging platform. They aim to design and improve business conversations. The platform is a continuous monitoring system to inform data-driven CX priorities by making the customer voice an effective operational tool.   

The AI Startups leverages Natural language processing (NLP) to understand and allow companies to listen to their customers. They scale their product across the many channels available for customer communications and make them actionable.    

11. Viz.ai   

Website: https://www.viz.ai/   

Domain: Healthcare Industry   

Founded in 2016, Viz.ai is a medical imaging company based in the USA. They specialize in applied artificial intelligence in healthcare and leverage advanced deep learning to communicate time-sensitive information.    

Viz.ai’s mission is to improve how healthcare is delivered in the world on a fundamental level. The intelligent software promises to improve access to care, reduce treatment time, and increase the speed of diffusion of medical innovation.   

In April 2020, Viz.ai launched a COVID-19 patient triage software, Viz COVID-19, to improve patient management. It will also allow for a safer hospital workplace during the pandemic.    

The startup has won the prestigious UCSF Digital Health Award for Best New Application of A.I.   

12. Luminovo   

Website: https://luminovo.ai/   

Domain- SaaS industry   

Found in 2017, Luminovo is a deep learning startup that helps enterprises in the electronic industry to develop tailored applications. The startup aims to bring innovations to everyone by reducing the time and resources needed. They are SaaS providers for the electronics industry.   

In April 2020, the startup raised a total of $2.5M in funding in a Pre-Seed round. It is also listed in Forbes’s list of Top 25 machine learning startups to watch in 2020.   

13. Rosetta.ai  

Website- https://www.rosetta.ai/   

Domain- Customer Service   

Founded in 2016, Rosetta.ai provides a deep learning platform. Their platform helps fashion e-commerce companies to understand consumers’ shopping behavior and preferences. This further helps them to personalize their on-site product recommendations.    

The AI Startups is based in Asia-Pacific and focuses on the fashion industry. They dive deeper into fashion, in apparel, cosmetics, and accessory, to build deep learning models and algorithms suitable for the use case.   

14. MixMode   

Website- https://mixmode.ai/   

Domain- Cybersecurity   

MixMode is an AI-driven cybersecurity startup to bring a third-wave, context-aware AI approach. Through this approach, it can automatically learn and adapt to changing environments.    

They have developed a predictive cybersecurity platform that is designed to reduce the number of alerts. They aim to deliver a continuous baseline of networks through their platform and allows users to focus on alerts that deserve their attention.   

Their AI-Powered Network Traffic Analytics Platform provides predictive threat detection and deep network visibility capabilities to enable the client to efficiently perform real-time and retrospective threat detection and visualization.   

Conclusion:   

In the last century, AI startups have become the pole-bearers of innovations and Innovation has been the backbone of humanity’s extraordinary progress. They have solved many daunting problems plaguing humanity.    

This blog just gives a glimpse of the most exciting AI Startups currently. However, there is an ocean of AI startups working towards developments in various areas of Artificial Intelligence.   

Author Bio:

Shreeya Chourasia is an experienced B2B marketing/tech content writer, who is diligently committed for growing your online presence. Her writing doesn’t merely direct the audience to take action, rather it explains how to take action for promising outcomes.

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