The facilitator is mobile machine learning or integration of machine learning in mobile apps.  

Big tech companies use machine learning to create those interesting reactions in their mobile apps. In addition to the use of artificial intelligence in mobile applications, integrating machine learning is mainstream nowadays. But mobile machine learning is not a cakewalk. It is neither walking on eggshells.  If you want to learn how to integrate machine learning into your mobile applications, then you are at the right place. Your next few minutes will be spent on reading (learning): 

• • • most common machine learning algorithms 
    • how to integrate machine learning into a mobile app development that is industry-specific 
    • best machine learning examples and how they work 

Before we move forward, let us take a glance at what machine learning is and why it should be integrated into mobile applications.  

What is Machine Learning?   

When we speak of the present, we are already talking about yesterday’s future. Our present and the upcoming future are defined by technology—which further drives machines. It is rather pensive to think how machines are an important part of our life. A machine has to be very sophisticated to learn on its own any behavioral patterns that we subconsciously follow. It is These machines not only imitate us but also follow our patterns quite precisely. The major driver behind this is machine learning.  

Machine learning is a branch or subset of artificial intelligence and computer science. It has the ability to automatically learn from the data without explicitly being programmed or assisted by domain expertise. ML focuses on the use of data and algorithms to imitate the way humans learn and gradually improves its accuracy.  

How is ML Beneficial? 

Following are the benefits of integrating machine learning: 

• 76% of businesses saw an increase in sales after ML integration 
• ML technology predicts better user behavior, optimizes processes, and leads up-sell and cross-sell 
• 50% of companies are using machine learning to improve their marketing strategies 
• ML helped several European banks increase their product sales by 10% 

Let us now focus on the various types of machine learning algorithms available for Android or iOS apps.  

“The more precise ML algorithms are made with more data, the better.” 

Machine Learning employs the following algorithms to build models that reveal connections: 

• • • Supervised learning is when an algorithm learns by using example data and the associated target responses. These data could include numeric or string labels, such as classes and tags. ML can then predict the correct answer if presented with new examples. 
    • Unsupervised learning.ML learns by looking at examples and not having to look for answers. The algorithm thus determines data patterns by itself. 
    • Reinforcement Learning. Developers train ML algorithms so that they can make certain decisions from the environment. This allows the machine to capture the most accurate knowledge possible and make precise decisions. 

Use of Machine Learning in Specific Industries 

Machine learning has various applications. It can be used in different industries to create mobile apps. We have noted down some ML use cases in mobile apps that are industry-specific.  

1. AI-powered financial assistant 

Let us understand how ML is used for financing. You can use various mobile apps to gain insights into your finances. These apps are usually developed by banks to offer clients added value. They use machine learning algorithms to analyze transaction history, predict future spending, track spending patterns and provide financial advice to users. For instance, Erica is a mobile voice assistant developed by Bank of America. Over Erica’s financial assistant Erica, Currency offers more personal and convenient banking for 25 million mobile app users. 

2. Mobile fitness apps with ML  

Various workout apps, powered by machine learning, analyze data from smartwatches, wearables, and fitness trackers. Based on their user’s goals, they receive personalized lifestyle advice. To create customized fitness plans, the algorithm analyzes user’s current health and eating habits. One of the most popular fitness apps that use machine learning is Aptiva coach. It offers a variety of workouts and even custom Aptiva workouts. The app also tracks user progress. 

3. Healthcare mobile applications for healthcare with ML 

Many condition-based mobile apps make it easy to track heart diseases, diabetes, epilepsy, migraines, and other conditions. These apps use machine learning algorithms to analyze user input and predict possible conditions. They also notify doctors about current conditions for faster treatment. 

4. Transport mobile apps 

Mobile apps for logistics, such as Uber Trucking or Fleet Management, must provide drivers with current information on traffic conditions. These apps then optimize roads based on current conditions to avoid traffic jams and deliver cargo on time. Developers integrate machine learning algorithms with traffic prediction software into road optimization mobile applications to receive this traffic information before they happen. This algorithm analyzes historical traffic data and predicts traffic patterns for a specific day and time. Learn more about machine learning applications in transportation by reading the article How AI is changing logistics. 

5. E-commerce 

Machine learning algorithms can be used in a variety of ways by online retail mobile apps. These algorithms can be used to offer more relevant product recommendations to buyers based on their purchase history, credit card fraud identification, and visual search. You can find more machine learning applications in mobile eCommerce apps by reading the article on how online apparel retailers can leverage AI to sell online. 

5 Common Examples of Mobile Machine Learning Integration 

Innovative algorithms improve the user experience on their mobile devices and bring new machine learning mobile app ideas. Below is a list of the top machine learning apps. 

1. Snapchat 

This application uses machine-supervised learning algorithms for computer visualization. The algorithm for computer vision was developed by Looksery, a Ukrainian startup. This company was soon acquired by Snapchat for $150 million. The mobile machine learning algorithm uses photos to find faces and add fun elements such as glasses, hats, ears, and more. We have provided a detailed explanation of how ML Snapchat filters operate in this article. 

2. Yelp 

The app uses supervised machine learning to improve user experience by recommending “Recommended For You” collections. The ML algorithm reviews each restaurant. The ML algorithm then determines which dishes are most popular based on how often the meal has been mentioned. Yelp also uses ML to collect, classify and label user-submitted photographs of dishes with different attributes. These attributes include “ambiance is elegant” and “good with children” with 83% accuracy. 

3. Facebook 

Facebook uses machine learning algorithms in many ways. After the ml algorithm has analyzed your profile, interests, current friends, and their friends, Facebook suggests new friends to you in the “People You May Know”. The algorithm can also pull in other factors to suggest people you might know. Facebook also uses machine learning in Newsfeed, targeted ads, and facial recognition. 

4. Netflix 

Netflix uses machine learning algorithms. It has incorporated precise, personalized references by using linear regression and logistic regression along with other similar algorithms. Netflix’s mobile app uses a diverse range of content based on variety, actors, user and critics’ reviews, and much more for its audience. This information is studied by machine learning algorithms.  

In the case of Netflix, ML algorithms are trained by user actions that track users’ behavior. These algorithms study what TV shows are mostly watched by users and the type of reviews received online. These algorithms are familiar with user behaviors and hence offer exceedingly personalized content.  

5. Google Maps 

Interestingly, Google Maps also utilizes machine learning algorithms to gather and study data from a very large number of people. Researchers on Google ask questions like how long it takes for commuting or if they face any difficulty to find vehicle parking. They derive, aggregate, and use this information by creating various training models from people who have shared their location information.  

Final Thoughts: Machine Learning and Mobile Apps 

Machine learning algorithms can improve customer experience, loyalty, engagement, and similar aspects. It is very suitable for any mobile app that requires predictions and leverages enough data.  

Today, machine learning has numerous applications, from banking to healthcare. Depending on the needs of your business, you may be able to leverage any one of these ML algorithms. Last but not least, you need to hire an experienced team to develop machine learning apps. 

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 Top 5 Examples of Machine Learning app development 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.

Only in the mid-2000s, the cloud, which we see today, was launched by e-commerce retailers like Amazon and virtual services like Google Docs.    

Since then cloud computing has only deepened its roots in the virtual world and started evolving. By 2021, many experts predict that 94% of the internet’s workload processing will rely on cloud computing, with companies switching almost all their operations to the cloud.   

Through the years, this progress of cloud has made it a crucial technology required in the business process. If your company is also relying on this breathtaking technology and wants to be at the top of the game. We have bought the future technology trends in cloud computing.   

This blog will also update you about the latest cloud computing trends and upcoming cloud technologies you should know in 2021. Still, with us, keep reading.    

1. AI and ML Adoption   

Artificial Intelligence (AI) and Machine learning (ML) technologies are among the top cloud computing trends that we are expecting to see growth in 2021.    

Though many businesses are already utilizing the technologies, AI and ML adoption include features such as product recommendation engines, image recognition, energy optimization, system failure monitoring, adaptive security learning, and language processing.   

The best part about these technologies is that they are not limited to businesses but can be used by consumers. They can use it to get better insights into products and interact with companies in a new way.   

The growth of AI and ML in the year 2021, will aid businesses through:   

• Security: ML will allow businesses to automate their security features to make decisions regarding program permissions and the efficacy of its malware detection. It can also keep an eye out for potential attackers and identify vulnerable areas.   
• Productivity: AI and ML can increase productivity by eliminating the need to do redundant tasks manually. Instead, machines will be handling these jobs without prompting or overseeing. This will leave employees to concentrate on other business areas and utilization of time and budget better.   
• Accessibility: Unlike before, AI has become readily accessible for all types of operations. AI features are even compatible with software for smaller-scale use and individual applications.    

2. Hybrid Cloud Services   

Companies have either private or public access to the cloud. Both of them have their pros and cons. But there is a new system coming up called Hybrid cloud.    

Unlike traditional models, the hybrid cloud refers to a combination of private and public cloud options. It includes on and off-premise equipment which gives them the flexibility to create the optimal setup.    

Such a cloud allows businesses to operate within their budget and use only the needed space. They also give them the liberty to do it without sacrificing any features.    

Hybrid clouds are great for smaller and medium companies with fewer resources but desire cloud-based service.   

In 2021, hybrid cloud services are expected to become widely adopted among businesses. They will become new hybrid cloud computing trends as they offer:   

• Security options: Hybrid cloud gives businesses the enhanced privacy of a private cloud and the benefits of public cloud applications.    
• Remote support: Hybrid clouds enable businesses for remote work for off-site employees. Also with a rising remote workforce, the hybrid cloud makes it easy to scale operations to suit changing needs.    
• Increased speed: Hybrid clouds allow the business to take on more tasks with less downtime, boosting operational speeds and reducing latency.   

3. Distributed Cloud   

This is one of the top trends in cloud computing due to the rise of the distributed cloud. The distributed cloud has become the solution for businesses that requires specific geographical or residency ties.   

The distributed cloud allows businesses to store and manage their data and operations that are tied to a certain location. Meanwhile, it also allows them to grant access to the public cloud and its features.    

The distributed cloud can result in shorter wait times and more privacy to the customers by processing data locally.   

In the future, the distributed cloud computing trend will see a few different phases, including distributed platforms similarly to hybrid clouds, industry-based settings, and a larger-scale variation.    

4. Streamline Entertainment   

Today, every household in the U.S has one or the other type of video streaming account. Video streaming services use the cloud to power their platforms. In the future, this trend will only grow and continue to proliferate into 2021.    

Cloud computing enables companies to host a rotating and scalable inventory. But, along with it, it enables consumers to enjoy entertainment and get rid of excessive buffering.   

Another type of cloud-based entertainment is cloud gaming which will continue to see growth. Similar to video streaming, cloud gaming allows consumers to access and play video games on cloud platforms. The cloud gives them better bandwidth and server capacity.   

Smaller or non-entertainment-related businesses can also harness the power of the cloud by interacting with their customer bases in new ways to make their marketing and public engagement powerful.    

5. Hyper-scale Data Centers   

This is the basic requirement for most the businesses in the digital age which is prompting the demand for hyper-scale data centers. A hyper-scale data center can quickly react to progressively heavy demand and scale dramatically.    

Instant consumption of data has driven organizations to work at a rapid pace. This also means that they need their IT frameworks to deliver services at a faster pace than any customary framework. Likewise, they need an IT infrastructure to scale at a rapid movement and provision their increased demand.    

Hence, the hyper-scale data center is another important cloud computing trend that will lead the industrial growth.    

6. Cloud-Native   

DevOps team’s primary focus is to create, manage, and improve processes. With cloud-native developments, DevOps can integrate application-specific designs. Besides, can do it with faster response times, merge platforms seamlessly, and utilize advanced tools.    

The demand for this cloud computing trend will continue to grow in 2021 as businesses grapple with the new changes. The companies will be more focused on scalability, higher security, and serverless technology.   

7. Serverless Computing   

Server technology trends continue to evolve once it is powered by the cloud. Serverless computing technology will partially or entirely eliminate the need for physical hardware on-site.   

With serverless technology, you will only be billed for space and features that your use. This allows businesses to focus less on staff, train, manage, budget for, and operate an entire server and more on their products, services, and customer relations.   

These serverless cloud computing trends will also allow businesses to:   

• Implement platforms with easier backend code   
• Minimize or eliminate wasted server space   
• Develop smarter applications and programs   
• Get faster turnaround   

Serverless solutions will continue to grow in the list of cloud computing trends in 2021. It will be popular because it includes pay-as-you-go models, access to more flexible options, and as-needed backend services.   

8. Disaster Recovery   

One of the most significant cloud trends is the need to have a scalable, usable, and accessible cloud-based disaster recovery (DR).    

In 2020, as most of the employees shifted to remote work, companies’ vulnerable areas were exposed which prompted a strong surge of cloud-based solutions and disaster response plans.   

When natural disasters or unstable internet connections threaten to interrupt operations, the cloud offers a solution that adapts to your changing situation. Cloud-based recovery allows staff to handle tasks from remote locations.    

Cloud-based recovery is simple-to-use technology that doesn’t require special training to operate. It includes more hands-off recovery websites that eliminate the need for ongoing manual support and troubleshooting.   

It is also a great solution for virtual attacks. Backing up files on a cloud server helps businesses continue their work without interruption and protect valuable data.   

Cloud computing enables businesses to take control of recovery time, flexible payment models, automated solutions, application portability, and gives them the ability to test and utilize disaster recovery resources.    

9. BPaaS, PaaS, SaaS, IaaS, and DaaS to Grow   

Cloud adoption projections say that businesses that relied on the cloud for 2020 will continue to do so as they move forward to 2021.    

Statistics say that the cloud computing market is booming and will grow by nearly 18 percent by the end of 2025.    

In the future, the need and demand for cloud computing services will continue to grow with no signs of stopping. Public and private cloud servers will become the standard for daily frontend, information storage, backend operations, and disaster recovery.   

Business Process as a Service (BPaaS), Platform as a Service (PaaS), Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Desktop as a Service (DaaS) will grow at a rapid pace. They are likely due to factors such as:   

• Accessible knowledge    
• Need for virtual technology for long distances   
• The desire for stronger security and privacy  
• Streamlined technologies   

10. Platform as a Service (PaaS)   

Among the above discusses cloud services, platform as a service will see the most growth in 2021. Cloud computing will increase the ability of businesses to build, manage, and run platforms for clients as a service.    

Unlike now companies will not rely on their resources to develop complex infrastructure, PaaS will allow third parties to handle it for them. Such services will be available for both private or public cloud platforms.    

PaaS will make it easier to store data, develop applications, test software, and increase stability across a platform for businesses.   

Small and mid-size businesses will also be able to take advantage of PaaS offerings to streamline their processes and track trends and patterns in data. Availability for PaaS will allow a lower barrier to entry with lower costs. Besides, there will be less need for equipment and trained professionals on-site.    

These services will provide consumers with lower costs options, but there will be a need for services to aid in organizing, managing, choosing, and utilizing those processes.   

Conclusion:  

In this blog, we have covered some of the most popular cloud computing trends in 2022. Do let us know in the comments below if we have missed your favorite.   

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.

The post Top 10 Game-Changing Cloud Computing Trends of 2022 appeared first on Tech Research Online.

Launched at a Google I/O conference as Vertex AI, the Google Cloud AI Platform team has been building a unified view of the machine learning landscape for the past few months. The platform brings AutoML and AI Platform together into a unified API, client library, and user interface.  

“Machine learning in the enterprise is in crisis, in my view,” said, Craig Wiley, the director of product management for Google Cloud’s AI Platform.   

“As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you — every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.”  

In this blog, we are going to dive in and understand everything about Google’s machine learning platform.   

What is There to Unify?  

The idea of unification is about the key constructs in machine learning:  

• Datasets are created by ingesting, analyzing, and cleaning the data  
• Then the model is trained (Model Training) which includes hypothesis testing, experimentation, and hyperparameter tuning.  
• This model is versioned and rebuilt with new data.  
• The model is compared and evaluated concerning existing models.  
• The model is used for online and batch predictions.  

Yet, how different the rest of the pipeline becomes depends on how you do your ETL (where do you store your data?). Don’t you think it will be nice to have the idea of an ML dataset? That’s what unification means which is behind the concept of a dataset.  

It’s also noteworthy that the deployment of a TensorFlow model is different from a PyTorch model. Even TensorFlow models might differ whether they were created using AutoML or by codes.   

You can treat all these models in the unified set of APIs that Vertex AI provides. Vertex AI provides unified implementations and definitions of four concepts:  

Datasets can be structured or unstructured. the platform manages metadata, including annotations, that can be stored anywhere on GCP.   

Training pipeline is containerized steps that are used to train an ML model using a dataset. It helps with reproducibility, generalization, and audibility.  

A model is a machine learning model with metadata. It was built with a Training Pipeline or directly loaded.  

The endpoint can be invoked by users for online explanations and predictions. It can have one or more models and model versions with disambiguation.  

The idea behind these artifacts is the same as the dataset or model or training pipeline or endpoint. It’s somewhat like everything is mixed and matched.  

Hence, you can the database for different models once it is created. You can get Explainable AI from an endpoint regardless of how the model is trained.  

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What are the Features of Vertex AI?  

• Supports all Open-Source Frameworks  

Vertex AI integrates with all the popular open-source frameworks and supports ML frameworks through custom containers for prediction and training.  

• Unified UI for the Entire ML Workflow  

The platform brings together the Google Cloud services. This enables them to build ML under one unified UI and API. Here, you can efficiently compare and train models using AutoML or custom code. Your models will be stored in a central model repository for deployment to the same endpoints.  

• Pre-Trained APIs   

Vertex AI has pre-trained APIs for NLP, video, and vision, among others. This helps to easily incorporate them into existing applications. It also helps in building new applications across use cases such as Speech to Text and Translation.  

AutoML allows businesses to train high-quality models as per their business needs. It leverages a central registry for datasets, such as tabular and vision.  

• Integration  

Developers can leverage BigQuery ML to create and execute machine learning models using standard SQL queries. They can also export the datasets from BigQuery into Vertex AI for smooth integration across the data-to-AI life cycle.   

What are the Benefits of Vertex AI?  

Google says that the AI platform requires fewer lines of code to train a model compared to any other platform.   

The platform’s custom model supports advanced ML coding and requires 80% lesser lines of code. The MLOps tools reduce the complexity of streamlines running ML pipelines and self-service model maintenance. Vertex Feature Store to serve, use, and share ML features.   

Google also states that without requiring formal ML training, data scientists can use the platform for tools to manage their data, prototype, experiment, deploy, interpret and monitor the models in production.  

To sum up the benefits, Vertex AI  

• Enables model training without code and expertise  
• Build advanced ML models with custom tools  
• Removes the complexity of maintenance of the self-service model   
• What is Vertex AI used for?  
• Creation of dataset and uploading data.  
• Training ML model on your data  
• Evaluating accuracy  
• Deploy trained model to an endpoint for serving predictions.  
• Sending prediction requests to the endpoint  
• Specifying a prediction traffic split   
• Managing models and endpoints  

What are the Tools in Vertex AI?   

• Vertex Feature Store  
• Vertex Model Monitoring  
• Vertex Matching Engine  
• Vertex ML Metadata  
• Vertex TensorBoard  
• Vertex Pipelines   

Use cases  

You can utilize this platform to ingest Cloud Storage and BigQuery data, and use Vertex Data Labeling to annotate high-quality training data. This will help them predict with more accuracy. Vertex’s feature can be used to share, serve, and reuse ML features. Vertex Experiments can be used to track Vertex TensorBoard and ML experiments to visualize ML experiments.  

The Pipelines of the platform can be used to simplify the MLOps process. Its training can help manage training services. Besides, Vizier on the platform offers maximum predictive accuracy, and prediction simplifies the deployment process of the models into production for online serving via HTTP.  

You can also get feature attributions and detailed model evaluation metrics. Moreover, Vertex ML Edge Manager, which is still in the experimental phase, facilitates seamless monitoring and deployment of automated processes with flexible APIs and edge inferences. This allows you to distribute AI on on-premise and edge devices across the private and public clouds.  

Deployment of models in the Vertex Prediction service offers easy monitoring of model performance. It will also alert you when the signals deviate, and you can find out the triggers and cause of the model-retraining pipelines.  

Vertex ML Metadata enables easier tracking of inputs and outputs to components in Vertex Pipelines for tracking. Lastly, you can track custom metadata from their query metadata using a Python SDK.  

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.

The post Everything You Should Know About Google Cloud AI Light up Machine Learning Platform   appeared first on Tech Research Online.

They say that we need a shift from reliance on the poorly curated datasets for new ML training models. Instead, their study recommends representation of a culture that cares for the people, respects their privacy, and property rights in their datasets.   

Also Read: Skylum Launches New AI Photo Editor with Luminar AI 

However, survey authors said that in today’s ML environment “anything goes.”  

University of Washington linguists, Amandalynne Paullada and Emily Bender along with Mozilla Foundation, fellow Inioluwa Deborah Raji, and Emily Denton and Alex Hanna, Google research scientists, wrote “Data and its (dis)contents: A survey of dataset development and use in machine learning”.   

The paper concluded that traditional language models can perpetuate prejudice and bias against marginalized communities. It also states the poorly annotated datasets as a part of the problem.  

They call for more rigorous documentation practices and data management. They say that the datasets made this way will require more time, effort, and money. However, they will encourage work on approaches to ML that go beyond the current paradigm.  

The paper reads, “We argue that fixes that focus narrowly on improving datasets by making them more representative or more challenging might miss the more general point raised by these critiques, and we’ll be trapped in a game of dataset whack-a-mole rather than making progress, so long as notions of ‘progress’ are largely defined by performance on datasets.”   

“Should this come to pass, we predict that machine learning as a field will be better positioned to understand how its technology impacts people and to design solutions that work with fidelity and equity in their deployment contexts.”  

In the past few years, events have brought to light the shortcomings of the ML community that might harm people from marginalized communities.   

On Wednesday, Reuters reported that Google has started carrying out reviews on sensitive topics of research papers on at least three occasions. Besides, according to internal communications and people familiar with the matter, the authors have also been asked to not put Google technology in a negative light.   

This came after Google fired Timnit Gebru due to an incident which the employee refer to as a case of unprecedented research censorship and yet a Washington Post profile of Gebru revealed that Jeff Dean had asked for an investigation on the negative impact of language models.  

The decision to censor AI researchers carry policy implications. But right now, Google, MIT, and Stanford are some of the most influential producers of AI research published in academic conferences.   

Earlier this month, in a Surveys and Meta-analyses workshop at NeurIPS, an AI research conference that attracted 22,000 attendees, “Data and its (Dis)contents” received an award from ML Retrospectives organizers.   

This year alone, NeurIPS published nearly 2,000 papers which also included work related to methods for faster, more efficient backpropagation; failure detection for safety-critical systems; and the beginnings of a project that treats climate change as a machine learning grand challenge. 

The post Artificial Intelligence Research Survey Found that Machine Learning Requires Culture Change  appeared first on Tech Research Online.

How Software development companies use AI-driven tools? 

Developing software is a very complex process because ideation, product definition, strategic designing, coding, quality assessment, and testing are not easy. Also, the ever-changing marketing trends and increasing desire for a better user-experience are making it even tougher. Humans have limitations, getting distracted, and tiring out from work is ubiquitous. Which means there is no guarantee of constant productivity. 

So, to take the Custom software development process to a whole another level, large enterprises have started using artificial intelligence and machine learning technologies. Although the technology is still budding and the full implication of them isn’t possible, companies use such technologies through some specifically designed tools to pave the way. 

Started by the large enterprises on test-bases, artificial intelligence, and machine learning tools are now applied industry-wide to get better productivity, efficiency, and work accuracy with no errors. To date, various tools are developed and used. Every tool has a specific purpose like data analysis, trend predictions, delivery estimation, requirements gathering, designing, compiling codes, intelligent testing, bug fixing, programming assistance, enhance decision making, and so forth. 

So, without any more delay, let us dive into the exciting details of some of the best and most reliable AI-driven tools that are delivering ground-breaking results. 

Tool#1 Google ML Kit 

Google experts have built this machine learning tool for mobile app developers to create custom features for Android and IOS mobiles. As this technology is specially optimized for mobiles only, it is very easy to use. Google ML Kit comes with a diverse range of vision APIs such as barcode scanning, face detection, tracking and detecting objects, link building, image labeling, text recognition, pose detection, and many more.  

Tool#2 Infosys NIA 

Infosys NIA is an AI-driven tool built-in 2017 by an India-based software development company named Infosys. With the help of this tool, software development companies can enable their clients to gather all kinds of organizational data from the business processes, technical and legal systems, records, individuals and store them into a self-learning database that is used to design further business strategies. The purpose of its development is to forecast market trends, sales, revenue generation, analyze customer behavior, and so on. Infosys NIA has enabled companies to manage their processes while efficiently serving their customers. 

Tool#3 IBM Watson

Want to gain a competitive advantage with an intelligent business process? If your answer is yes then, IBM Watson is the perfect pick for it. Software development companies use this AI tool to empower your business-process and get the benefits of accelerated R&D, enriched interactions, scaling the technologies and expertise, anticipating market trends, risk mitigation, and so on. This Artificial intelligence technology has enabled business teams to focus on their high priority creative tasks. 

Tool#4 Tensorflow

This open-sourced math tool allows software development companies to design, develop, deploy, and experiment with counterfeit systems with high-volume data. Tensorflow is an AI-driven multi-layer computational tool that can do some deep learning calculation for research and production purposes. With additional advantages like robust machine learning, easy prototyping,  powerful experimentation, and being able to operate on any device, CPUs, and GPUs, it also has a disadvantage of having a longer learning curve. 

Tool#5 Accord.NET  

It is an AI-driven framework that consists of machine learning technology to integrate the libraries with C# language. End-users have a wide range of choice because of its multiple layers. It is being used in software development services for its powerful features such as self-learning algorithms, signal processing, scientific computing, pattern recognition, computer vision, and so forth. 

Tool#6 H2O

H2O is an AI-driven tool written in many programming languages like JAVA, Python, R, etc. It is developed to build mobile applications for machine learning, data analysis, risk analysis, customer intelligence, and predictive analysis. It is a cloud-based and Apache Hadoop supported database. Businesses could grow by deriving insights from the datasets of H2O. 

Tool#7 Mxnet 

MxNet can render the software development company with an ecosystem that enables them to explore various tools and technologies to support the development process. Apart from being integrated into eight languages like scala, Julia, Clojure, C++, R, Java, and Pearl, it has an additional benefit of being scalable for training, research, production, and performance optimization. The MxNet tools and libraries can be extended to computer vision, NLP, and others. 

Tool#8 Deeplearning4J

Being a machine learning technological tool written in Java and Scala language, DeepLearning4J is used in software development services for rapid prototyping. While built to fit in the micro-infrastructure, it could also run across the business system with distributed CPUs and GPUs. This AI-enabled tool supports AWS, Python, ScalaAPIs, Hadoop, and Java and offers a special toolkit for DevOps, data scientists, and data engineers. 

Tool#9 Google Assistant 

Yes, it is one of the most common AI-driven tools and every smartphone in the world has it. This tool may seem common but it can support various languages of the world, Google Assistant can browse any detail at your command, set reminders, have a two-way conversation, schedule your meetings, and so forth. In short, this tool can be used to enhance the efficiency and productivity of the business team involved in the rendering of software development services.

Tool#10 Cortana 

Cortana is a virtual assistant developed by Microsoft. Though it has many similarities with Google Assistant like supporting many languages, setting reminders, having conversations and all but the voice recognition feature is much more powerful. And the main advantages are that it can perform some specific human tasks too. So the software developers are using such virtual assistants to cope up with their daily less-productive tasks. 

Conclusion

Artificial intelligence and machine learning technologies are the results of coding and now, these same technologies are armed in powerful tools helping to make, compile, and strategize new codes. And we are seeing the results, AI and machine learning techniques which are currently practiced in the industry enable a software development service provider to deliver a high-end product that enhances the organizational efficiency, increases work productivity, and ultimately delivers outstanding results. 

It won’t be too much to say that shortly, we might get a chance to see some more powerful tools because software development companies nowadays are investing more and more in R&D of such fields. But for now, these tools are

working wonders in the software development industry and changing people’s lives for the better. What’s your opinion about it? Feel free to share right here in the comment section below. 

Author Bio:

Reena Maheshwari is an analyst in a Software app development company at Tatvasoft. She works with the development and sales team for the development of projects.

The post 10 Amazing AI-Driven Tools to Innovate Your Software Development Process appeared first on Tech Research Online.