Why Should Companies Adapt to AI?

Posted by Parija Rangnekar On Filed under Artificial Intelligence, Machine learning app development No Comments
Why Should Companies Adapt to AI?
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AI, big data, and machine learning technologies are increasingly ingrained in our daily lives. It is no longer the future but rather a present reality. AI allows you to make decisions much quicker and more precisely than before. While it is still a novel idea, it already has many businesses uses.
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It is altering the way things are done and making people more productive. In fact, 86% of CEOs say that AI is a mainstay in their workplaces as of 2021. It’s becoming vital in unforeseen ways, from forecasting customer behavior to decreasing data entry.
Even if individuals have mixed opinions about AI, it is hard to deny that it provides us with enormous prospects. It is particularly true from a financial perspective since commercial enterprises and government agencies are interested in this field.
Before considering why companies should adapt to AI, let’s look at what artificial intelligence is.

What Is Artificial Intelligence?

Artificial intelligence (AI) is the foundation for simulating human thinking functions by developing and deploying algorithms in a dynamic computing environment. Computers are pretty good at evaluating these algorithms and coming up with the best decision. Artificial intelligence (AI) and machine learning (ML) are the core future of commercial decision-making.
Machine learning algorithms are used to build and deploy AI. ML refers to the tools and techniques used to create a model to identify patterns. Machine learning model operations are required in businesses where multiple models are deployed.
Developing, analyzing, changing, and implementing predictive models are part of machine learning model operations. It keeps track of inspections, pauses, routines, statistics, and versioning to ensure repeat testing. It makes the machine learning lifecycle so much simpler.
All modeling operations attempt to make ML models as efficient and productive as possible. It’s worth noting that we’re dealing with two different aspects of machine learning model maintenance. AI is used in various domains, enabling our lives to be more accessible than ever before. Artificial intelligence can aid any company in the following ways:
  • Process management optimization
  • Using market research to get insights
  • Including models in the manufacturing process
  • Involvement of stakeholders in the findings

Benefits AI Can Provide to Companies

  • Improves Customer Service
    • Chat will have surpassed all other customer support platforms by the next few years. By automating client contacts, AI-driven chatbots enable businesses to deliver 24/7 customer assistance. AI advancements have enabled bots to pick up on conversational nuances and precisely imitate human language.
    AI-enabled chatbots can bridge customer service voids for small organizations that don’t have the funds or human resources to hire a customer care staff.
    AI can also help in customized alerts to specific users. Personalization allows it to be tailored to particular users, assuring that they obtain the most suitable response at the right time.
    Machine learning techniques are now being used in SEO services too. It is used to analyze the purpose behind query phrase picks and the content of queries.
    Artificial intelligence in Customer Service
  • Save Time and Resources
    • Companies can benefit from AI’s increased efficiency and production because manual processes take time and cost. Automation has substantially impacted all corporate sectors by reducing repetitive and tedious processes and conserving time and resources. Processes include:
    • Operate robotic lines in manufacturing
    • Monitor warehouse balances
    • Process payments
    • Respond to customer queries
    AI can complete jobs at a rate and level that no person can achieve. When humans are not obliged to execute repetitive and tedious jobs, they may focus on higher-value activities that machines and computers can do.
    Once the initial startup expenses are covered, automating activities results in fewer labor hours, less paperwork, and improved customer satisfaction. As a result, you’ll be able to increase your profitability and reallocate cash to produce more revenue.
  • Helps in HR Processes
    • The selection process is another place where artificial intelligence may enhance productivity. AI can accelerate the applicant assessment process by automating filtering calls and examining applicant submissions. AI also aids in the elimination of human bias in pre-employment checks, which is a positive thing for employee engagement.
    Human resources frequently manage interior employee assets. According to the Harvard Business Review, internal services for addressing problems in IT and personnel regulations can be made easier with artificial intelligence.
    One of the causes is that artificial intelligence may be used to drive natural language search for discovering answers to specific questions. AI improves each time, allowing it to respond to requests more rapidly and correctly.
  • Improves Cybersecurity
    • Artificial intelligence is an attempt to mimic human understanding. In the sphere of cybersecurity, it has immense promise. AI platforms can be trained to provide threat warnings, discover potential malware, and protect critical data for companies.
    It can be used to detect cyber dangers and potentially dangerous behaviors. Conventional software solutions cannot keep up with a large amount of new malware released each week. Therefore, this is an area where artificial intelligence can help.
    Systems are trained to identify malware and execute predictive modeling using sophisticated algorithms. It can provide information on new anomalies, cyberattacks, and countermeasures. After all, hackers are subject to the same trends as the general public, so what’s trendy with them shifts regularly.
    Artificial intelligence in Cybersecurity
  • Easy Insights
    • A startling amount of companies have yet to tap into their data riches. Companies usually have all the information about the consumers but don’t know what to do with it or draw essential insights.
    AI helps firms make intelligent, strategic business decisions by combining large volumes of complex data, analyzing it, recognizing patterns, and uncovering insight.
    For example, AI is being used in the financial services industry to organize, categorize, and pattern massive volumes of economic data. Its goal is to deliver more personalized and customized advice to clients. In a few minutes, AI can process vast amounts of data.

Final Thoughts

You may have observed that all of the highlighted advantages are rather broad. Different companies in various industries may use AI to achieve multiple goals in practice. AI may increase efficiency, reliability, and customer support and assist a firm in developing by spotting patterns and maximizing sales prospects.
Another benefit of artificial intelligence in the company is marketing personalization. Algorithms can spot interconnections and repeating patterns in the behavior of prospective and actual users. Based on this information, making particular offers for certain persons makes it feasible.
The list could continue indefinitely. However, the real benefits of AI are not contained in what most people believe in. So, it’s crucial to figure out how it might benefit your company specifically.
Would like to build an impeccable AI/ML solution for your business? Well then, the Machine learning Services offered by Biz4Solutions are worth a try! Our team of tech nerds has the proficiency, experience, and expertise required to tailor highly functional AI/ML apps/solutions for clients from diverse industry verticals.

What are the differences between Machine Learning and Deep Learning?

Posted by Parija Rangnekar On Filed under Machine learning app development No Comments
What are the differences between Machine Learning and Deep Learning?
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The technological marvel, Artificial Intelligence, has evolved significantly to give rise to two other ingenious technologies – Machine learning and Deep Learning. Both of these technologies have created a buzz in the software market and are setting new trends by executing unthinkable tasks. ML and DL are opening up new avenues for new-age entrepreneurs by making way for intelligent and intuitive software solutions. Entrepreneurs, these days, are roping in a Machine Learning Company for designing disruptive solutions for them.
Although Machine Learning and Deep Learning are subsets of the same technology – Artificial intelligence – they are quite different from each other. And, new-age businesses planning to leverage the technical benefits of these amazing technologies, must understand their differences well, so that they are able to implement these technologies correctly.
This post provides deep insights into Machine Learning and Deep Learning and explores their differences.

Machine Learning: An Overview

Machine Learning is a subset of Artificial Intelligence. It provides a system with the capacity to learn as well as improve from the experience gained, without the need for being programmed to that level. Data is employed for training and then finding the correct outcome. Machine Learning solutions perform a function using the data fed to it and progressively improve with time.
This technology is used for executing all types of automated tasks across several industrial domains right from data security companies for identifying malware to finance businesses who want to receive alerts for favorable trades.
Machine Learning is classified into 3 categories
Supervised Learning: This approach involves a wholly governed learning process, wherein the result is predicted based on a set of training samples provided with training labels also called the classifying data point. Here machine learning developers tell the algorithm what to predict during the training time, hence the name supervised learning.
Unsupervised Learning: This approach does not get training labels for the training samples. Here, the algorithms are created in such a manner that they are capable of finding suitable patterns and structures within the data provided. Similar data points are assembled together after the consistent patterns become apparent. Various data point appears in different clusters. It projects high-dimensional data into low-dimensional ones, for visualizing or analyzing.
Reinforced Learning: This approach involves a robot-like agent that performs actions and quantifies outcomes to learn how it should behave within a given environment. It follows the MDP (Markov Decision Process) – receives a reward point for making a correct response. This expedites the confidence level of the agent and encourages it to take up more such functions.
Example:
When ML is applied to an on-demand music streaming service, its task is to find out what new songs/artists to suggest to specific groups of listeners. For making decisions about such recommendations, an ML algorithm relates the user’s preferences with those of other users with similar musical tastes.

Deep Learning: An Overview

Deep learning, a subset of ML, is a technology where recurrent neural network and artificial neural network comes together. The formation of algorithms is quite similar to that of ML, only with the difference that there are more algorithms levels involved. All of these networks combine to form a layered structure of algorithms termed the artificial neural network – it’s just like the biological network of neurons present inside a human brain. Deep learning solutions continuously analyze data with a logical structure, just like the processing that happens inside a human brain to draw conclusions.
Deep Learning applications can solve complicated problems by processing the algorithms and is way more capable than the standard ML models.
Multiple layers that are stacked between the input and output layer
  • Input layer consisting of a time series data or pixels of an image
  • Hidden Layer called weights; it’s learned while the neural network is being trained
  • The output layer is the final layer that provides a predictive analysis based on the input that has been fed into the network.
Example:
The Google-developed gaming app named AlphaGo is a perfect example of Deep Learning implementation. A computer program has been created using a neural network for playing this abstract board game against professional players. And, AlphaGo has successfully defeated world-famous players of the Go game – an instance of artificial intelligence defeating human intelligence.
Deep learning is also used for functions like translation, speech recognition, and operating self-driving cars.

Key Differences between Machine Learning and Deep Learning

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Let’s now explore the key differences between Machine Learning and Deep Learning based on the following parameters.
Basic Functioning Principle
Machine learning is a super-set of Deep learning that takes in data as an input, then parses the data and makes decisions based on the learning while being trained. Deep learning, on the other hand, is a subset of ML, here data is accepted as an input for making intelligent and intuitive decisions using a layer-wise stacked artificial neural network.
Machine learning solutions are apt for solving problems that are simple or partly complex; whereas Deep Learning models are suitable for solving more complex problems.
The Type of Data involved and the Problem Solving Technique
Machine learning solutions usually deal with structured data and hence, employ traditional algorithms such as linear regression. Deep learning models can work with structured as well as unstructured data as they depend on the layers of an artificial neural network. Machine Learning algorithms parse data in parts and after processing these parts separately, integrate them to produce the final outcome. Contrarily, Deep learning systems follow an end-to-end approach – take in the input for a problem and produces the end-result directly.
For example, a program has to identify specific objects – license plates of cars parked in a lot – within an image; find out the objects’ identity and location. With an ML solution, this task will be executed in two steps – detecting the object and then recognizing it. Using a Deep Learning application, the task will be completed at one go – you input the image and the identified objects along with their location appear in a single result.
Data Dependencies and Output
Machine Learning handles thousands of data points and its outputs include numerical values or classifications. Deep learning, on the other hand, handles millions of data and its outputs range from numerical values to free-form elements like text and speech.
ML depends on a large amount of data, yet can function smoothly with a smaller amount of data as well. But this is not the case with deep learning models – they perform well only if humongous data is fed to them.
Algorithm Usage
ML employs different kinds of automated algorithms for parsing data and turns them into model functions for predicting future actions or making informed decisions based on the learning acquired from collected and processed data. Data analysts detect these algorithms for examining particular variables within sets of data.
Deep Learning structures the algorithms in layers to build an artificial neural network. With this approach, data passes through several processing layers for interpreting data features and relations. This neural network is capable of learning and then forming intelligent decisions on its own.
Hardware Requirement
ML programs are less likely to be complex as compared to deep learning algorithms. Machine learning programs need a CPU to process and so, can function on conventional computers or low-end machines without the need for high computing power. Deep learning algorithms, on the other hand, require way more powerful hardware as well as resources; because of the complex nature of the mathematical calculations involved and the need for processing a huge amount of data. They use hardware like GPUs or graphical processing units, and this increases the demand for power. GPUs possess high bandwidth memory and hide latency while transferring memory on account of thread parallelism.
Feature Extraction Methodology
The Deep learning mechanism is an ideal way of extracting meaningful functions out of raw data and is not dependant on hand-crafted features such as a histogram of gradients, binary patterns, etc. Moreover, the feature extraction methodology is hierarchical – features are learned layer-wise. As a result, it learns low-level features from the initial layers and as it goes up the hierarchy, more abstract data representation is learned.
However, ML is not a suitable option when there is a need to extract meaningful features from data. This is because, for good performance, it is highly dependent on hand-crafted features provided as input.
The degree of human intervention needed
ML needs continuous human intervention for obtaining the best results. Deep learning does involve a more complex set-up procedure, but once set up requires very less human intervention.
Execution Time involved
Machine Learning algorithms consume much lesser time for training the model, but testing the model is time-consuming. On the contrary, Deep learning applications take much lesser time to test the model but take a bit longer to train the model.
Industry Readiness
It’s easy to decode ML algorithms and it can interpret which parameters were picked and why those parameters were chosen. Deep learning algorithms, on the contrary, are simply a blackbox and are capable of outshining humans in regards to performance. Thus, ML solutions are better bait for industry application as compared to Deep learning solutions.

Final Verdict

Machine Learning and Deep Learning are here to stay. Both of these technologies possess a huge potential in transforming every industry vertical. Dangerous tasks such as working within harsh eco-systems, activities concerning space travel, etc. are expected to be replaced by ML and DL models in the near future. So it’s high time to be well versed with these outstanding technologies.
However, developing and implementing ML and DL solutions is no cakewalk and so, it’s advisable to hire experienced professionals for this purpose. For technical assistance in designing, deploying, and maintaining, ML/DL models, Biz4Solutions, a highly experienced and competent outsourcing software company in India, would be a good choice. We have extensive experience and expertise in dealing with ML and DL systems for global clients.