Tag: ML
No longer the stuff of science fiction, artificial intelligence (AI) and machine learning (ML) are revolutionizing the way customers interact with brands. Businesses that have embraced these technologies can reshape the customer experience, curate one-of-a-kind buyer journeys, and strengthen bonds with their target audiences.
As your organization works to remain competitive in the modern business ecosystem, it must tap into the power of AI and ML technologies to provide a superior customer experience.
How Are AI and ML Enhancing Customer Experience?
Artificial intelligence and machine learning solutions can profoundly impact every facet of the customer experience. By leveraging these technologies, your business can:
1. Facilitate Hyper-Personalization
Customers who interact with your brand are looking for a personalized experience. As such, brands that put their products and services at the center of attention instead of prioritizing experience will miss the mark. Likewise, blasting your customers with generic advertising content or sending them broad, basic messages simply won’t cut it anymore. Instead, you must personalize every interaction to deliver timely and relevant content to each user.
Artificial intelligence and machine learning technologies facilitate a level of hyper-personalization that was thought to be unachievable just a few years ago. In a 2022 Salesforce survey, 88% of consumers reported that an experience provided by a company is almost as important as the product. Using AI and ML technologies, you can personalize customer experiences by utilizing real-time data, like their browsing history, purchasing habits, etc.
Artificial intelligence and machine learning solutions can also eliminate friction from the customer journey. For instance, AI- and ML-powered chatbots can leverage information from past interactions to create personalized messages for each consumer. This will minimize customer frustration by reducing how often consumers are asked to repeat information they have previously provided.
2. Allow Customers to Stay Connected 24/7
Customers expect access to timely and relevant support around the clock. However, staffing your customer support department 24/7 is financially infeasible. So how do you bridge the gap between customer expectations and the fiscal limitations of your business? AI and ML solutions are the clear answer.
With artificial intelligence and machine learning technologies, you can provide your customers with access to automated support like chatbots. These bots can respond immediately to customers and resolve many basic product- or service-related issues without tying up your customer support staff. This capability will not only allow you to reduce the workload on your team but also help you provide more timely and omnichannel service to customers, no matter when they reach out for assistance.
3. Conduct Predictive Behavior Analyses
The sooner you can identify consumer behavior trends, the better your chances of capitalizing on emerging opportunities. Unfortunately, traditional analytics solutions do not facilitate real-time decision-making because they often rely on data that is days (or even weeks) old.
The good news is that artificial intelligence and machine learning technologies enable you to conduct predictive behavioral analyses using real-time data, guiding your decision-making processes and enabling you to adapt to emerging trends like never before.
4. Enhance Your Understanding of Target Audiences
Artificial intelligence and machine learning technologies allow you to step into your target audience’s mind. You can use these newfound insights to guide your digital marketing strategies, refine products and services, and enhance the customer experience.
Due to how AI and ML learn and evolve, these technologies will only become more effective over time as they get access to more data, better helping you anticipate how your target audiences are likely to behave in the future. This enables you to proactively eliminate friction points from the buyer’s journey and paves the way for increased sales and better profitability.
Read more: Is AI-powered mobile app what you need for your business now?
Use Cases: Major Industries that Have Embraced AI and ML
Artificial intelligence and machine learning technologies are going mainstream, and many industries are taking advantage of these powerful tools for both B2C and B2B interactions. Business leaders in these sectors understand that these technologies will significantly impact their organizations’ ability to compete, both now and in the future.
Some of the industries that are using AI and ML technologies on a broad scale include:
- Software development
- Language processing and transcription
- Retail
- Customer service
- Marketing
- Manufacturing
- Finance
- Agriculture
- Logistics and transportation
- Healthcare
The healthcare and logistics sectors were some of the earliest adopters of artificial intelligence and machine learning technologies, whether by predicting the likelihood of patients developing certain diseases or by providing customers with more accurate shipping estimates. These industries (and every other on this list) utilize AI and ML technologies to enhance the customer experience.
These technologies also provide meaningful insights into the efficiency of business operations. Organizational leaders can use the information gleaned from these technologies to proactively address critical organizational growth hurdles and promote business continuity.
How Your Business Can Optimize Customer Experience with AI and ML
Artificial intelligence and machine learning technologies will empower your business to revolutionize the customer experience along every meaningful touchpoint. First and foremost, these technologies will help your business truly understand the customer journey and its impact on organizational profitability. And once you understand the state of your business and how well it is currently managing the customer experience, you can begin using your AI and ML tools to refine the customer experience.
If you want to maximize your return on investment, consider incorporating artificial intelligence and machine learning technologies into as many business processes as possible. You can use these solutions to automate redundant processes, hyper-personalize advertising content, and refine the customer experience from top to bottom.
Read more: Use cases and business benefits of deploying Machine Learning!
Tap into the Power of AI and ML with Fingent
Are you ready to harness the power of artificial intelligence and machine learning so that you can provide your clients with the experience they deserve? If so, then it is time to explore a partnership with Fingent.
At Fingent top custom software development company, we provide customized artificial intelligence applications and machine learning solutions. Cumulatively, these technologies will differentiate your brand in the competitive digital marketplace and enable you to modernize the customer experience.
To learn more about Fingent’s suite of services and solutions, contact our team today. Together, we can reshape your customer experience and set the stage for the growth of your business.
Machine learning is changing the face of everyday life, science, and business. It is revolutionizing all industries, from advancing medicine to powering various cutting-edge technologies. Though Machine learning (ML) was a part of AI’s evolution until the 1970s, it evolved independently. It has become a chief response tool for cloud computing and eCommerce.
The goal of machine learning in business is to adapt to new data independently and make decisions and recommendations based on thousands of analyses. Machine learning enables systems to learn, identify patterns and make informed decisions with minimal human intervention.
Today, ML is a necessary aspect of modern business. It uses algorithms and neural network models to improve the performance of computer systems. Machine learning in business and manufacturing is enabling organizations to achieve notable strides. These strides include increased performance and efficiencies, improved processes, and enhanced security.
This article will discuss the benefits of machine learning in business and its use cases.
Remarkable Benefits of Machine Learning for Businesses
According to Fortune Business Insights, the global market size for machine learning in business is expected to grow to USD 209.91 billion by 2029, exhibiting a CAGR of 38.8% during the forecast period. ML has been and continues to scale operations tremendously. Across industries, ML has led to a boom in affordable data storage and faster and more reliable computational processing.
Here are six remarkable benefits of machine learning in business:
1. Automation for better decision-making
Most businesses find themselves wasting precious time sorting through duplicate and inaccurate data. Such businesses benefit from using the predictive modeling algorithms of ML in their processes. Such a process will understand duplicate inaccurate data and distinguish the anomalies. It enables the organization to avoid inaccurate reporting that can result in poor customer retention.
Instead, this will allow businesses to use their accurate database to detect wasted costs, missed opportunities for sales, and revenue capital. In addition, organizations can overcome challenges and risks that arise due to miscommunication or poor performance metrics. Thus, businesses can streamline their operations and improve decision-making which could be translated into better ROI.
2. Increased scalability with minimum expense
Semi-supervised machine learning algorithms can help organizations leverage useful insights from customer profiles and enable them to view their brands from customers’ perspectives. Doing so will equip organizations with relevant insights to build their brand by improving their products and services.
3. Predictive maintenance
Predictive maintenance that ML aids manufacturing firms’ power to follow best practices that lead to efficient and cost-effective operations. The historical and real-time data predict problems and stipulate strategies to solve those problems. Plus, workflow visualization tools can eliminate issues and unwanted expenses incurred due to those issues.
4. Financial analysis
ML can greatly assist as it gathers and analyzes large volumes of quantitative and accurate historical data. It is used for portfolio management, loan underwriting, fraud detection, and more.
5. Personalization
Using machine learning in business will allow organizations to know their customers better and provide them with a more personalized customer experience. Organizations no longer need to rely on guesswork because ML models can process different types of information collected from numerous sources and provide relevant data about their customers.
6. Cybersecurity
ML technologies can improve cybersecurity to solve cyberattacks once and for all. Empowered by ML, intelligent security programs can gather and process data about cyber threats and respond to them in real time. ML models can detect the slightest deviations in patterns and flag them. Or destroy an attack in its nascent stage.
Read more: Can Machine Learning Predict And Prevent Fraudsters?
Top Use Cases
Machine Language has made its mark across industries and found a place in many different applications. Here are some top use cases:
1. Enhanced social media features
Businesses can use machine learning algorithms to create attractive and effective social media features. For example, ML algorithms in Facebook enable it to identify and record a person’s activities. These activities include records of chats and the amount of time that person spends on each post. It uses this data to determine what kind of friends and topics may interest that person and accordingly make suggestions.
Read more: Why Time Series Forecasting Is A Crucial Part Of Machine Learning
2. Product recommendation
Product recommendation is an advanced application of machine learning techniques. It has been the most popular application of almost every eCommerce website today. This technique allows websites to track a consumer’s behavior based on their previous purchases, search patterns, and cart history. It enables the website to make apt product recommendations to that consumer.
3. Recognition
Image recognition is one of the most significant and notable ML and AI techniques. It is adopted further for pattern recognition, face detection, and face recognition.
4. Sentiment analysis
Sentiment analysis is a real-time ML application. It determines the emotion or opinion of the speaker or the writer. For example, a sentiment analyzer can detect the thought and tone of a written review or an email. It can analyze the review-based website, decision-making applications, and more.
5. Access control
Most large businesses are actively implementing ML models to determine the level of access an employee should be granted. This application of machine learning can ensure the security of the organization.
6. Bank Domain
Banks are using ML to prevent fraud and protect accounts from hackers. Machine learning algorithms determine what factors to consider in creating a filter to prevent an attack.
Read more: Machine Learning – Deciphering the most Disruptive Innovation
How Fingent Can Help with Deploying the Best of ML
Leveraging the capabilities of machine learning in business can open the door to many opportunities. It is wise for any organization to take advantage of ML rather than lag behind competitors. However, we understand if you have questions. That’s why Fingent software development experts are here to help you. We can deploy the best machine-learning models efficiently and smoothly.
As a partner, Fingent can work with your team as you take on digital initiatives for sustainable business growth. We enable our clients to make data-driven decisions by efficiently deploying machine learning in business. Our cost-effective services will save you a considerable amount of time and money.
Furthermore, we do not follow a one-size-fits-all strategy. We provide custom software development services that cater to your needs. Therefore, look no further if you are looking for a reliable, efficient IT partner to deploy the best machine-learning models.
Better businesses need better cyber security.
Regrettably, threats to cyber security have become the new norm across public and private sectors. The pandemic affected all types of businesses. If anything, uncertainties around remote working amplified cybercrime. As a result, cyber security’s importance has become more clear now than ever before.
As cyberattacks become more sophisticated, businesses will have to stay one step ahead. Security professionals need strong support from advanced technologies like Artificial Intelligence (AI) to protect their companies from cyber threats.
AI can enable security teams to handle greater and more complex threats than ever before. More specifically, it has proven to identify and prioritize threats. In some cases, AI has even taken automated action to quickly remediate security issues. This article considers how AI can redefine the cyber security needs of an organization.
Before we discuss further, let’s find out the impact cyberattacks can have on businesses.
How Cyberattacks Affect a Company’s Performance and Value
Protecting a company against cyber threats is costly. It can impact the relationship between your company and your customers.
Unfortunately, cyber threats are never static. Millions are created each year and are becoming more and more potent.
In a survey conducted by Hiscox-Cyber Readiness Report, 28% of the businesses that suffered attacks were targeted on more than five occasions in 2020. Companies have lost millions to such cyber security breaches. Companies like financial services, technology, and energy were hit the hardest.
That is not all. Cyber security breaches have caused several other damages including:
- Outlays such as insurance premiums and public relations support.
- Operational disruption.
- Altered business practices.
- Business’s standing and customer trust.
- Stolen intellectual property including product designs, technologies, and go-to-market strategies.
- Legal consequences.
Read more: Quantum Vs Neuromorphic Computing – What Will the Future of AI Look Like!
How AI Contributes to Cyber Security
Cyber threats are real and certainly worrisome to businesses. It is important to protect critical digital assets.
However, it takes planning and commitment of resources. With good security operations, you can stay on top of most of the most serious cyber threats. True, there may be smart thieves, but Artificial Intelligence can provide smarter security.
Here are 5 specific ways AI can contribute to cyber security:
1. Robust Zero-Day Malware Detection
Malware is unpredictable. And signature-based tools will not detect attacks that have never occurred before. Given that, is it possible to defend against something unpredictable? Yes!
AI is cable of grasping all the possibilities and finding relationships that traditional security tools would miss. While traditional security strategies have their place in cyber security, they are insufficient to detect and prevent zero-day attacks.
Zero-day attacks are best detected by automatically identifying aberrant behavior and alerting administrators immediately. AI can enable organizations to be more proactive and predictive with their security strategies.
Artificial Intelligence provides visibility and security for an organization’s entire data flow. AI helps organizations gain such visibility by dismantling each incoming file to search for any malicious elements. Simultaneously, it also looks at the user and network behavior and anomalies from expected activities.
Together with ML, AI adapts its behavior to new network conditions, constantly adapting to evolving security conditions. Even those hackers who use modern ML penetration methods cannot be fool AI-enabled cyber security.
We cannot stop security breaches from happening. But Artificial Intelligence helps organizations avoid potential disruptions before attackers wreak havoc.
2. AI Can Safeguard Large Amounts of Data
Whether a company is small or mid-sized, there is a lot of data exchanged between customers and the company every day. This information must be safeguarded from potential cyber threats. Cyber security experts cannot always inspect all the data for potential threats.
AI is the best option to detect threats to routine activities. Because of its automated nature, AI can sift through large amounts of data in real-time and identify any hazards lurking amid the chaos.
Read more: Artificial Intelligence and Machine Learning – The Cyber Security Heroes Of FinTech!
3. AI Takes Care of Redundant Cyber Security Operations
Hackers constantly modify their methods but the fundamental security practices do not change. Plus, they may weary your cyber security worker.
Artificial Intelligence takes care of redundant cyber security operations while imitating the best of human traits. It also does a thorough analysis of the network to locate security flaws that may harm your network.
4. AI can boost response time
Ideal security is the one that can detect security threats in real-time. The principle of ‘a stitch in time saves nine’ applies here.
Integrating AI with cyber security measures is a sure way to detect and respond to attacks immediately. Unlike humans, AI does not miss a spot when examining your system for risks. Besides, it can detect risks early, thereby boosting response time.
5. Authenticity Protection
Most websites allow users to log in and access services or make purchases. You will need greater protection as such a site contains private information and sensitive material. To maintain customer trust, it is important to ensure all data about your guests remains safe while accessing your site.
Artificial Intelligence can provide an enhanced security layer. AI can secure authentication when a user wishes to connect their account. Login measures like CAPTCHA, fingerprint, and facial recognition are used to determine if the attempt is legitimate or not.
Read more: Safeguarding IT Infrastructure from Cyber Attacks – Best Practices
Do Not Be Afraid!
Fingent is your reliable security partner. We provide professional security with reliable service. As a proactive security partner, we look ahead to ensure your business is successful far into the future.
Using AI’s real-time monitoring capabilities, we can spot potential issues before they become a major problem. Security experts Fingent are aware that cyber security threats are not limited to work hours. Our professionals here at Fingent software development experts will be there for your business whenever you need us.
We are in business today because of the reputation we built with our customers. We offer a unique level of enterprise IT support, and our clients can rest easy knowing that their business is always protected.
Give us a call and let’s discuss your security needs.
Businesses are always on the lookout for ways to optimize processes and gain greater visualization. When processes work efficiently, the output of the product is higher. This leads to workflows that run smoothly with minimum errors and higher capacity. A good reason for the growing popularity of process automation and visualization.
Automation and visualization are the future of the business strategy. Gone are the days of carefully filling in graph paper by hand. Today, process automation and visualization help enterprises up their game by allowing access to real-time models capable of accurately capturing the nuanced data sets.
In this blog, we will expand on how enterprises can up their game with process automation and visualization.
How Enterprises Can Up Their Game with Process Automation and Visualization
Data visualization enables human operators to manage vast sets of data, glean insights from different information sources, and perform operations more intuitively and strategically.
In the current data-immersed world, data visualization can significantly add value by conveying large datasets visually. What does this mean for your business? This means a better grasp of critical customer data.
According to the IDC, the collective sum of the world’s data is predicted to grow to 175 zettabytes by 2025. Processing such large amounts of data can become a problem.
By allowing automation and the right programs to sort out your business data, you can generate graphs. You will be able to use these graphs to up your game in business competition.
Data visualization offers businesses the hope of getting a grasp on data. Fortunately, the human brain can process and recognize trends, identify potential issues, and forecast future development from clear visual displays.
Read more: How Powerful Is Data Visualization With Tableau
Look Out for Upcoming Powerful Trends in Automation and Visualization
1. AI and ML
Artificial Intelligence and Machine Learning render visualization more accurate and efficient. These technologies enable businesses to handle customer feedback without bias. Process automation allows you to sort the feedback in real-time and according to your specifications.
2. Unlock Big Data with Data Democratization
Large amounts of data are hard to understand. It requires data scientists and other experts to unlock its treasures. Not anymore. Advanced no-code data analysis platforms can automate your data process. This is called the democratization of data.
Democratization of data leaves it malleable and easy to display allowing your employees any level of tech support. When this is paired with the data visualization type, it can unlock big data results for teams at all levels of your organization.
3. Video Visualization Is Here to Stay
Young and old alike tend to retain the information they see over what they hear. This would mean that video infographics will be the future.
Video applications for business strategy and customer retention are key areas for future strategic data visualization implementation.
4. Real-time Visualization for Early detection
Knowing a problem at the exact moment it arises can assist businesses in customer retention and brand presence. Early detection can have a dramatic impact on the bottom line.
Process automation can help run a dashboard that allows users to submit their error reports to your customer support. Then the reviews can be tagged and analyzed using sentiment analysis.
5. Mobile Optimized Visualization
An increased number of people access the internet on their mobile devices. Your business needs mobile-optimized data visualization to stop customer churn.
It enables you to know if your potential customers are learning about your services through social media or an online review board. Though mobile-optimized visualization is an easy step, it is critical to keep your business on top of the game.
Read more: 7 Awesome Data Visualization Tools
Business Applications of Process Automation and Data Visualization
1. Financial Service and Insurance
The finance service industry is a prime candidate for process automation and data visualization. Two top requirements of this industry are customer response time and compliance with strict regulations.
When automated, quick decisions can be made based on pre-defined rules like loan applications, claims processes. businesses can use data visualization to make reliable predictions or risk calculations in the financial industry.
Insurance fraud can cost billions of dollars damage. Process automation and data visualization can improve fraud detection.
Read more: Deploying RPA for Finance, Healthcare, and IT Operations.
2. Distribution and Logistics
Process automation and data visualization can minimize costs by planning transport promptly, reducing costs of downtimes and maintenance.
3. Sales
Data visualization can greatly improve relationships with your customers. It helps you know the needs of your customers better, and address each of them directly in real-time.
4. Marketing
Data visualization and process automation can reduce marketing costs substantially. These technologies can help evaluate the demographics, location, transactions, and interests of your customers. Visualizing these details can help you understand their purchase patterns.
Thus, data visualization can be used to create and target new customer segments. Cross-selling is another advantage. At the same time, data visualization may reveal that customers are dissatisfied. Identifying this and responding quickly can counteract the situation to retain your customer base.
5. Healthcare
Process automation and data visualization enable cheaper healthcare. It can help predict disease occurrence and proactively propose countermeasures.
6. Science and research
Visualization enables the evaluation of the data of an experiment. Process automation and visualization can be advantageous especially when an experiment generates large amounts of data within seconds.
7. Production
Large amounts of data are generated during production. Using process automation and visualization can help plan preventive maintenance and prevent production delays or downtimes.
Prepare Your Business For The Future With Fingent
Fingent helps enterprises automate document-based processes. We can help you create safer sharing and collaboration. Our platform allows you to create teams, assign roles and privileges, and streamline communication.
Fingent’s partner integrations allow you to use it together with your existing software. Our top-level measures protect our users’ data. The encryption we provide ensures content integrity and prevents alteration.
Fingent top custom software development company can help your organization reach the goal of paperwork elimination. Doing so can lead to efficient resource distribution throughout the organization.
What’s more, it reduces carbon footprint. Our experts bring along specializations supported by scientific rigor and in-depth knowledge of advanced techniques to design, develop, and deploy solutions for process automation and visualization.
Give us a call today and let’s get talking.
Disclaimer: This is an opinion piece. The views expressed in this article are mine and does not represent my employer.
Smart, sentient machines! The latest (well, not really) hype! Look back a week or two, and think about the number of days you went without hearing about how AI is going to change your career, health, medicine, food, travel or whatever. Television, newspapers, and blogs remain constantly flooded with announcements about the imminent disruption <insert field here> that is going to witness due to using AI.
Let me show you some, ahem, examples.
We have here (in the order of increasing horror):
- AI-powered Air Conditioners
-
AI-powered Washing Machines
Source – Gizmodo
-
AI-powered Suitcases
Source – Indiegogo
-
AI-powered Phones
-
AI-powered Toilet
Source – The Verge
-
AI-powered Underwear!
Okay, I made that last one up. But for a second there, you guys did believe me, right? RIGHT?
That is the sad state of affairs. We are all techies here, and might think “wait, WHAT?”. But the vast majority of the not so technical audience out there sees AI as magic. They see it as something beyond their cognitive ability to process and accept any BS branded as “AI-powered” without questions. Thus, we have this article!
Source – Mashable
So what is the truth with AI? If you dig deep enough, or if you peel off enough layers(pun intended), what is happening?
Before we move on to taking the buzz off of buzzwords, let’s look at some core concepts.
Related Read: Top Artificial Intelligence Trends to Watch Out for In 2019
What is AI?
From wiki, Artificial intelligence is intelligence demonstrated by machines. It is the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
But Really, What Is Artificial Intelligence?
IM[not so H]O, AI is just a buzzword. Really, it is just meaningless jargon. Okay, maybe not meaningless, but it’s still jargon. Don’t believe me? Let me give you some examples:
- Computers playing checkers and beating the best human players was considered AI. Until it was not when it was accomplished around 1994 by Chinook, the checkers-playing computer program.
- Computers playing chess and beating the best human players was considered AI. Until it was not when it was accomplished around 1997 when IBM’s Deep Blue defeated the then world champion, Garry Kasparov.
- Cruise control was considered AI. Until it was not when it started being available in production cars in 1990+(partial) and 2010+(full speed range).
- Automatic parking was considered AI. Until it was not when it started being available in production cars somewhere around 2006.
- Human speech recognition was considered AI. Until it was not when it started being available as Google Assistant, Cortana, Siri, etc. Now we have a real-time speech translation!
I could go on, there are quite a few examples of this phenomenon, formally known as(yes, it is so well known that it has a name) the AI effect [wiki].
So a much better definition of AI was put forth by Douglas Hofstadter.
“AI is whatever hasn’t been done yet.”
– Douglas Hofstadter
Just Computation
“Every time we figure out a piece of it, it stops being magical; we say, ‘Oh, that’s just a computation’.”
– Rodney Brooks
So, if it’s all just computation, why was it not, well, “computed” earlier?
Yes, computation, or rather, the capacity for computation is the key. A lot of problems were characterized as AI because, at the time, algorithms for solving that were not known yet, or because the resources to compute those were not available yet.
-
Availability of Computation Power
Eg. Chess/other games, etc.
Moore’s law and the explosion in storage availability have played a major role in turning the tables. [It is important to note that the tables have not turned completely. Yet. There is so much more ground to cover.]
-
Availability of Unbiased Data
Eg. Natural language processing (NLP).
Okay, now you may be thinking “Enough data was not available for speech recognition? This guy is full of BS”, but hear me out. With the explosion of social networks, so much content is created and made freely available that finding huge swaths of unbiased(this is the key here) voice/video of natural speech is available, which in turn has helped the advances in NLP.
-
Availability of Infrastructure
I guess I don’t have to mention the improvement in internet speed that happened over the decade. This has accelerated content creation, real-time processing, etc.
So, What is All the Current Hype About?
The hype is not current. There has been huge interest around AI from the time it was first proposed around the 1950s. The sheer number of films about it tells us about how much.
But the current wave of hype and buzz surrounding AI comes from the recent advances made in, drumroll please, Machine Learning.
What is Machine Learning?
Machine learning is
- giving computers the ability to learn
- to find patterns in data
- from experience
- without explicit programming.
ML is essentially about classifying and predicting stuff.
The typical operation is something like:
- Take some data
- Learn patterns in the data
- When presented with new data, classify it for the best guess of what it probably is, based on the “learning” that happened in [2].
Related Read: Machine Learning- Deciphering the most Disruptive Innovation
Meh! So what is the big deal?
Once trained for one purpose, the same ML system can be reused(with additional training) to learn new concepts. This can be done without rewriting the code. Now that is a big deal.
Let’s look at a simple example: Classifying emails.
Traditional programming:
if the email contains "it's never a job, its always a career" then send to trash; if the email contains ... then ... if the email contains ... then ...
ML programs:
try to classify some emails; change self to reduce errors; repeat;
That was a two-minute primer on Machine Learning. So next time someone starts talking about Artificial I, I hope you feel the pang and say “Excuse me, I think you mean Machine Learning, not AI”.
Source – HubSpot
What Machine Learning is and why it is important in business
Given the increasing prevalence of data today, machine learning has evolved rapidly to advance computing a step forward. Enabling computers to automatically learn and improve performance using data, ML is viewed as a disruptive technology with the potential to transform areas like enterprise, manufacturing, healthcare, e-commerce etc. Global ML adoption rates points to huge investments and diverse use cases in the coming years making it one key technology to focus your efforts.
This infographic focuses on the essentials of machine learning together with how it helps in uncovering insights from huge data stacks. Besides, its applications, global adoption rates, and future developments are also dealt with to give you a basic understanding of machine learning so that it can be put to use for uplifting your business processes and operations.
Deploying proper machine learning systems can uplift your business and maximize the ROI. Get in touch with us today to discover and implement the right machine learning solution that will transform your business altogether.