This article will cover what a beginner should know on how to start using AI in your business. It will also discuss the benefits of doing so and how you can get started.

What Is The Advantage Of Using AI In Your Business?

So you may be wondering how AI can improve customer experience. Well, AI can help you identify issues that customers experience. It can detect them and then recommend solutions.

For example, you can use AI to record customer feedback on their purchasing experience. Or, you can use AI to automate certain customer service tasks, such as responding to frequently asked questions. It will take charge of those tasks that can be easily automated, eliminating senseless expenses and helping you gain some time.

Ways In Which You Can Implement AI In Your Business

If you want to start using AI technology in your business, you should consider a few factors. We will go over some of them below:

1.  Understand What AI Can And Cannot Do

AI can help you scale certain processes and make better decisions by providing more accurate data. However, there are certain things AI cannot do. For example, AI cannot ascertain a customer’s emotional state as well as a human can. Therefore, AI might not be your best solution if your business requires a lot of empathy or human interaction.

You also need to consider the cost of implementing AI into your business. If you have the budget, AI can greatly assist your company. But if you do not, it may weigh against your gains. It is known that deciding whether or not to implement AI into your business is a tough decision. As a result, you must weigh the pros and cons and decide what’s best for your company.

2.  Consider Why You Need the AI

As a business owner or manager, it’s important to consider your aims. Why do you want to implement AI into your business? What are you hoping to achieve? How will AI help you reach your goals?

You can use AI in many different ways in your business. One approach is to use AI to automate tasks currently being done manually. It can help improve efficiency and free up employees’ time to focus on more strategic tasks.

Another approach is to use AI to supplement or replace human workers in certain roles. It can be particularly beneficial in repetitive roles or those requiring high accuracy, such as data entry.

No matter your approach, it’s important to ensure you implement AI in a way that aligns with your business goals.

3.  Constantly Test The AI

It’s important to test the AI. It is required to do so even for a period after implementation. All these precautions aim to ensure that it functions as intended. Then, you can monitor the AI’s performance and compare it to expected outcomes. If the AI is not performing as intended, you can make adjustments to improve its performance.

It’s important to clearly understand how the AI is intended to function before you begin the testing. It will help ensure that the results of the test are accurate. You should conduct the tests in a controlled environment to identify and address any unexpected results. After the test is complete, it’s important to analyze the results and ensure that the AI functions properly.

The Bottom Line

As you can see, you can use AI technology in many ways in your business. By following the tips in this guide, you can get started with AI and begin reaping the benefits it has to offer. So what are you waiting for? Start using AI today and take your business to the next level!

Also Read: Artificial Intelligence And Machine Learning In Controlling Are In Advance

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Before explaining these two technologies, it is necessary to remember the definition of their origin: Artificial Intelligence. This resides in the capacity of a machine to process the data it captures, which in turn are the result of previous experiences. This processing is similar to the functioning of the human brain, which captures information and transforms it to generate knowledge.

What Are Machine Learning And Deep Learning?

First of all, machine learning, as a derivative of AI, involves the creation of algorithms that can modify themselves without human presence.

Machine learning is a data analysis method based on the premise that our systems learn from that data. Through such a method, plans will be able to identify patterns and make decisions without any intervention on our part.

Deep Learning

On the other hand, deep learning is a type of machine learning whose function is to train a system to learn by itself. This competition is possible by recognising patterns and executing tasks such as those that we human beings do. As a relevant fact, this branch of AI uses a specific class of algorithms called neural networks.

Although the algorithms are created and work similarly to machine learning, multiple layers of neural networks are responsible for individually providing a different interpretation of the data. These networks have the purpose of trying to imitate the function of the neural networks of our brain. This includes voice recognition, object detection, and image identification. Voice assistants such as Amazon’s Alexa or Apple’s Siri are based on this technology. Even biometric recognition systems for fingerprints, face, voice, etc., also have this type of technology.

Machine Learning And Deep Learning Have Different Capabilities.

A deep learning model is designed to continuously perform data analysis while maintaining a logical structure similar to a human being. To achieve this type of analysis, deep learning must use layers of algorithmic systems, those mentioned above, artificial neural networks. These networks are the ones that allow much more advanced knowledge than the basic machine learning models. Deep learning facilitates the automation of training processes and is capable of creating its criteria automatically, altogether dispensing with human intervention.

In short, machine learning and deep learning are almost the same, as they work in the same way but have different capabilities. Although the basic models of machine learning are continually evolving, their functions still require monitoring on our part. Suppose an artificial intelligence algorithm gives an incorrect prediction. As a result, we will have to intervene and apply the necessary adjustments. By having this model, an algorithm will be able to determine if a prediction is incorrect by utilizing its neural network.

Advantages Of Machine Learning and Deep Learning

Many organizations have basic or advanced Artificial Intelligence applications, and their use continues to spread.

Regardless of the productive sector or the size of these companies, implementing this technology helps solve common day-to-day problems to the most complex. For this reason, this technology has a very positive impact on efficiency and profitability.

In particular, companies that manage large amounts of data must rely on machine learning and deep learning; since these resources can be used in different areas, from finance and health to marketing and sales.

We can summarize part of the advantages of AI in these areas:

• Speed ​​in the management and processing of data and identification of relevant information.
• Ability to analyze consumer behavior with greater precision.
• Fraud detection and prevention, specifically in the banking and insurance sector.

Even machine learning and deep learning support making the right decisions in companies. Likewise, they increase the capacity for efficient and intelligent work, reducing the percentage of human error and adding competitive advantages.

How Can Machine Learning And Deep Learning Be Helpful in Our Company?

Machine learning and deep learning contribute decisively to our company obtaining scalability, more excellent performance, and cost and time savings. In addition, these technologies can also provide the following benefits:

• They personalized customer service. It allows analyzing user preferences so that personalized products can be offered automatically. In this way, the perception that customers have of our company is improved, thus enhancing loyalty. For example, platforms such as Netflix, YouTube and Spotify constantly use this technology to suggest other content based on what we have enjoyed. 
• Process automation. Of course, one of the most relevant contributions of the two technologies that we analyze is the automation of routine tasks. The latter absorbs a lot of time and effort from human talent and does not provide added value. Using machine learning, our systems can detect the processes they must deal with.
• Reduce errors. The automatic learning of the management systems applied in the organization means that the mistakes made are not repeated. The longer it stays in the system, the more resilient it will be.
• Preventive actions. Based on the above, machine learning tools can prevent bugs and errors. Artificial Intelligence can exclude any action that compromises or puts the development of products or services at risk.

Other Important Uses

• Cybersecurity. Undoubtedly, the contribution of this technology to the protection of networks, systems and terminals of organizations against risks of cyberattacks is significant. It should be noted that most malware uses similar code, so the use of machine learning can prevent their meddling.
• Fraud detection. Thanks to the technologies we are dealing with, it is possible to detect which transactions are legitimate and which are not accessible. It is even feasible to reveal the mismanagement of resources. Such a function is achievable when a pattern is assigned to financial movements.
• Medical diagnoses. When implemented in the technological tools of the health system, these technologies help insurers be more intuitive about possible health problems, depending on the frequency of medical consultations. Apart from that, these technologies offer more reasonable costs and recommend different medication options, among others.
• Improves the security and integrity of information. Cloud storage is another service that facilitates these two strands of AI.

The post Machine Learning And Deep Learning Increasingly Necessary For Companies appeared first on TechReviewsCorner.

These bots are a fast and easy way to answer customer service questions and provide support. They can be used in various business scenarios, and they can have a significant impact on your customer service strategy. This article explores five ways how chat bots can boost your customer service.

1. They Reduce the Cost of Customer Service

Customer service is expensive, and with a chat bot, you can drastically decrease the time it takes for someone to fix a problem or offer advice. You can use chatbots to provide the same level of service but at lower costs.

This can mean that you can save money by cutting the number of representatives you employ, which is especially useful if your staff turnover rate is high or your employees are expensive.

2. They Help You Build Trust and Reputation with Your Customers

While there’s no substitute for good old-fashioned customer service face-to-face, most customers prefer online interactions and expect the same levels of service online as they would in person. They also expect you to be professional and provide accurate information about your products and services.

With chat bots, you can use them to respond to customers more quickly and personally. This will help build trust and reputation.

3. They Can Help You Get New Leads from Social Media

Social media is an excellent resource for companies since it constantly provides new leads, such as those who follow their online posts or comment on them within the social media app of their choice.

Chat bots can provide your company with the ability to receive live feedback from these people. This will help you retain existing customers and build more interest in your business and product, which will lead to more new contacts and sales opportunities.

4. They Provide Management Tools that Enable Better Communication between Departments

Customer service representatives often need to relay information between different departments within a business so that each department knows what the other employees are doing at any given time. This can be difficult to manage since you can’t always be in two places at once to make sure that everyone understands the situation as it unfolds.

As such, chat bots can be extremely useful when it comes to providing your business with a solution. They offer employees access to their customer service representatives anytime, anywhere.

5. Chat Bots are Easier to Train than Traditional Automation Systems

Traditional automation systems typically require months of training on all of the systems within each automated system before they are ready for use. On the contrary, working with chat bots can be quite easy as they don’t need any training. This means that your employees won’t have any difficulties using modern automation software and will quickly gain skills at using them.

It makes them more productive and efficient within their daily duties and reducing human error while they are operating these automation processes.

Conclusion

As the internet becomes an increasingly integral part of our daily lives, businesses of all kinds are looking to leverage the power of online communication and customer service. The bots automate the process of communication with customers and look to augment, rather than replace, human service representatives.

They are one of the most popular ways businesses are looking to provide customers with an exceptional level of service without increasing operational costs.

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This article tells you how you can make the most out of AI apps and use them to make your daily life more productive and streamlined. According to Gartner, the worldwide artificial intelligence software market will reach a valuation of $62 billion in 2022, which is why it is important to know how you can benefit from AI-powered apps.

Facial Recognition Technology

One of the ways in which you can embed artificial intelligence in your life is through the facial recognition technology that is present on your smartphones and other security devices. Thanks to this software, you can unlock your phone and also access sensitive apps through biometrics by simply bringing your face in front of the camera. The camera captures an image and uses machine learning to compare it with a stored image of your face. If it determines that your face matches the image, the phone will be unlocked.

Social Media

Social media apps have also come a long way, and there is no chance that you don’t check your Facebook, Twitter, Instagram, and other accounts several times throughout the day. In all of these apps, artificial intelligence is always working behind the scenes and provides you with a personalized experience. The social network picks up on the content that you like to view the most, and uses it to provide you with tailored posts, friend suggestions, filtering out inappropriate or fake content, and also preventing cyberbullying and other malicious actions.

Grammar Tools

If your job or academics require you to write several emails or draft various documents, you can benefit from grammar checking and correction tools like Grammarly and the spell check feature in Google Docs, which make use of artificial intelligence to check for spelling and grammar. Grammarly, and other similar apps, also look for sentence structure, tone, and other factors. You can also set the tone that you are aiming for, and the writing tool will offer personalized suggestions as to what you can change to achieve the desired style.

Virtual Assistants

Have you ever used Google Assistant or Siri on your Android or iOS smartphone? These virtual assistants are powered and driven by artificial intelligence, which allows them to provide you with a human-like experience. They use natural language processing and machine learning to receive and understand your voice or text input and provide you with the most suitable solution or response. You can instruct it to make a call, send a text message, set a reminder, book an appointment, tell the weather, book tickets for travel or the movies, and do much more.

Smart Home Devices

One of the best ways to leverage the wonders of artificial intelligence in your daily life is to make use of smart home devices, which are designed to automate most of the operations that go on daily inside your house. Examples of these devices include Google Home, Amazon Alexa, Amazon Echo, and also several types of smart thermostats, which can adjust the temperature inside the home according to certain factors. Plus, there are smart refrigerators that create shopping lists full of items that aren’t inside them, and also and also offer recommendations. By using these applications and appliances, your life becomes much more convenient.

Daily Commute

Artificial intelligence has also revolutionized the way people navigate and find their way around their city or state. Apps like Google Maps make use of artificial intelligence to provide you with real-time traffic conditions and updates, as well as personalized suggestions regarding the best route to your destination, the amount of time spent on each possible route, any roadblocks or hurdles that come in the way, and much more. You can also make use of the AI capabilities of Google Maps and other apps to help you avoid traffic or find the best route to work or school.

Banking

Apart from the other applications we have discussed, artificial intelligence has also penetrated the baking sector, and it is used to ensure the security of transactions, as well as to detect and prevent all kinds of fraud. You might have a banking app on your phone that allows you to deposit a cheque simply by scanning it or receive an alert every time your account balance goes below the threshold or do much more. All of these functions are performed with the help of AI, and it helps in validating each transaction as well, thus keeping your banking and financial information safe from cyber attackers.

Shopping Recommendations

Another way in which AI can help make things easier for you is through personalized shopping recommendations on e-commerce stores, such as Amazon, Alibaba, and several others. These platforms make use of AI and machine learning to understand your purchase history and search patterns and also learn what people like you like the most. Then, they provide tailored recommendations for what you might like to buy. These apps even comb through your search history on various platforms so that they can show you products you are most likely to buy.

Streaming Services

Be it YouTube, Netflix, Amazon Prime Video, or any other video streaming platform, none of them can function without artificial intelligence. These services offer you suitable recommendations for movies, TV series, and documentaries based on the type of content that you usually watch. It takes into account various factors to do so, including the genre, actors, time period, duration, and much more. Most of the stuff that you watch is recommended by the platform, instead of you searching for it.
This concludes our guide on how you can make the most out of AI apps. As you can see, artificial intelligence has made its way into nearly every industry, which is why it is hard to imagine spending even one day without our smartphones or AI-powered apps.

Also Read: Explainable And Reliable Artificial Intelligence

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This article aims to give you an overview of machine learning paradigms and the types of problems they are commonly used for.

Machine Learning Paradigms

As a general rule (there are exceptions), machine learning algorithms build a model representing the knowledge they have been able to extract from the data provided as input. Depending on the additional information supplied to the algorithm, we can differentiate between different paradigms to guide the learning process. Below I briefly describe the best known:

• Supervised learning. It consists of indicating to the algorithm, as it learns if the output it has generated for a particular case (the prediction) is correct or not. The most common action is for the algorithm to adjust the model it generates each time it is told that it has made a mistake to improve its predictions.
• Unsupervised learning. The only information that is delivered to the algorithm is the data samples without further details. From these samples, it is possible to analyze the distribution of the values, the similarity or distance between the models, the degree of concurrence of some variables with others, etc. The applications are multiple, as we will see later.
• Semi-supervised learning. It is a case halfway between the previous two. From the available data set, the correct output is known only for some samples. The algorithm uses them to build an initial model that, later, provides a forecast of the output value for the rest of the pieces. In this way, the model is expanded and adjusted, taking advantage of the available information.
• Reinforcement learning. The algorithm to which the data is provided is not supplied with the accurate outputs to adjust its model, as is the case in the supervised point. Still, it is awarded a more or less significant prize depending on how well the sequence of actions is carried out. In this way, the behaviour is reinforced towards the objective pursued.

These paradigms allow specific types of problems to be solved and implemented using different tools: the models that represent knowledge. Depending on the chosen model: a tree, a neural network, a set of rules, etc., a specific algorithm will be used to generate and fit it.

Types of Problems in Machine Learning

Machine learning is used to solve a wide range of real-life problems. These problems, or tasks as they are also known, can be categorized into a few types. Although it is not a strict rule, each situation is usually addressed through a specific learning paradigm. For this reason, the most common types of tasks are outlined below according to the paradigm with which it is traditionally approached.

Supervised learning Tasks

There are two fundamental types of problems that are solved by supervised learning, described below. The actual outputs, known in advance for the data, will allow the algorithm to improve its model parameters. Once the teaching or training of the model is completed, it will be able to process new samples and generate the appropriate output without any help.

• Classification. Each data sample has associated one or more nominal outputs, called class labels, labels, or simply class. To automatically classify, a predictive model is created, to which, by delivering the input variables, it generates the corresponding class labels as output. A classifier can be used to process credit or risky loan applications, differentiate incoming email messages as spam or essential, find out whether or not a person’s face appears in a photograph, etc.
• Regression. As in the previous case, each sample also has an associated output value, but in this case, it is of an objective type (continuous, not discrete, that is, with possible results within a continuum), so the techniques used to generate the model are usually different from those used for classification. However, the procedure for fitting or training the model is similar: known accurate outputs are used to correct its parameters and improve prediction. With a regression model, it is possible to determine the height of a person based on their sex, age and nationality, or to predict the distance that will be able to travel a transport taking as input variables the weight of the load, the volume of fuel available and the ambient temperature.

Also Read: What Are Artificial Intelligence (AI), Machine Learning (ML), And Deep Learning (DL)?

Unsupervised Learning Tasks

As indicated above, the types of problems faced with this learning paradigm are characterized because the data samples only have the input variables. There is no way out to predict that can guide algorithms. Therefore, the models generated, if they exist, are not predictive but descriptive. The most common tasks are:

• Grouping. Analyzing the similarity/dissimilarity of the data samples, for example, calculating the distance they are from each other in the space generated by the values ​​of their variables. Several disjoint groups are created. This technique, also known as clustering, facilitates visual data exploration and can be used as a primary classification method when the required class labels are not available to generate a classifier.
• Association. The search for associations between specific values ​​of the variables that make up the samples is carried out by looking for the concurrence between them, that is, by counting the times they appear simultaneously. As a result, this type of problem can generate a set of association rules, a technique widely used in all kinds of electronic and physical businesses to arrange their products or recommend them.
• Variable reduction. By analyzing the distribution of the values ​​of the variables in the set of samples, it is possible to determine which of them provide more information, which is correlated with others and therefore are redundant, or whether it is possible to find an underlying statistical distribution that generates these data, which would simplify its original representation. There are many possible techniques in this type of task, from the selection and extraction of variables to manifold learning, consisting of finding the aforementioned underlying distribution.

Other Types of Learning Tasks

A vast majority of the problems addressed through machine learning fall into the categories listed in the previous two sections. However, there are other types of tasks that require different approaches. An example would be optimization problems in general, of which perhaps the best-known exponent is the travelling salesman. This task consists of finding the shortest itinerary to visit in cities. When n is enormous, the problem becomes unapproachable to the exhaustive search: evaluating all the possible alternatives to determine the best one.

There are many other cases within this category, and the difficulty is usually always the same: the optimal point is not known, so it cannot be known whether a potential solution is more or less good, and the number of possible solutions, or steps to reach them, it is enormous. There are two categories of techniques that are commonly applied to deal with these problems:

• Bio-inspired algorithms. This group includes genetic algorithms, evolutionary strategies, optimization based on particle systems, etc. All of them start from the same concept: reproduce mechanisms existing in nature such as evolutionary selection in living beings, the behaviour of flocks of birds, colonies of ants, etc. Thanks to them, it is possible to find an acceptable solution to the optimization problem in a reasonable period.
• Reinforcement learning. This paradigm, described at the beginning of the section, can also be applied to optimization problems, although in recent times, it has gained notoriety for its success in learning to play and win certain games.

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When people talk about modern work today, one often hears the terms digitization and artificial intelligence. Still, almost as often, they talk about robots that will change the world of work. But what is a robot? Why is it called that, and when did it become built as a robot for the first time? And above all: What types of robots are there and what can they be used for? If you are toying with ​​using a robot, you should know what kind of machine you are dealing with.

What Are Robots?

The answer to this question is not that easy because there is no uniform definition. We have come across the term robot quite often recently: lawn mower robots, vacuum cleaner robots, toy robots, social robots for pensioners, or a robot dog. The short form ‘bot’ can be found in so-called chatbots or the Google bots that scour websites in the computer world. The camera drone or autonomous driving is also sorted here again and again. And then, of course, there are the industrial robots, which are mainly known from the automotive industry. What they all have in common is that they do something automatically.

So is every automatic machine a robot? No. A device is first and foremost a powered (motorized) tool that humans control. A drill as well as an automobile or a coffee machine.

An automat is a machine that can perform a specific task automatically, but only this one. So the coffee machine can brew coffee by itself, but nothing else. And a CNC milling machine can be programmed, but in the end, it can only mill. If this particular process is no longer required, an automat is useless.

On the other hand, a robot can be programmed and converted and can therefore be used for a large number of tasks. The specialty of a robot is its flexibility. Where a machine can only repeat the predefined work process, a robot can be reprogrammed so that it grips or assembles, advertisement.

The History of Human-Like Robots

What Was The First Robot, and Who Invented it?

People have always invented things that make their work easier. And the desire, on the other hand, for a mechanical helper who tirelessly takes on complex and tedious tasks is probably as old as humanity itself. Even Aristotle thought of automatically working machines with the ancient Greeks. But it would be a good 2,000 years before the world was ready. From a purely mechanical point of view, inventors were prepared to build sophisticated automatons as early as the late Middle Ages. But it was only the transistor technology invented in the 1940s that laid the foundation for it to program it.

When we think of the articulated arm robots that are typical today, we cannot ignore George Devol and Joseph Engelberger. With Unimate, these two developed and built the first robot that electronics could program. As early as 1954, inventor George Devol applied for a patent for a programmable arm. However, he initially lacked funding to implement the ambitious project. In 1956 he met the investor, entrepreneur, physicist, and engineer Joseph Engelberger at a cocktail party. He was a big fan of Isaac Asimov’s robot stories and was immediately ready to join the project. Engelberger took part so much enthusiasm for his work that today he is considered the father of modern robotics.

What Are So-Called cobots?

Robots are usually made of metal and, thanks to the electric motors used, are also powerful. They can move very quickly under certain circumstances. Therefore, there is, of course, a considerable risk of injury if a human gets in the way of a robot during operation, which is why most classic robots operated behind a protective fence. But there is another way.

With these devices, a new type of robot was founded: the cobots. The name is made up of the English terms Collaborative Robot, i.e., robots for human-machine collaboration. To achieve the necessary security, the manufacturers of cobots use different methods, sometimes in combination. They measure the forces that their motors generate and compare them with the planned path. Some have force-torque sensors in some or all of the joints. Some use an air-filled sensor skin, others use capacitive sensors (like a touch display). They all have in common that as soon as they encounter an obstacle or a person, they stop.

To further reduce the risk of injury, the edges of the cobots are rounded, and they travel at reduced speeds and forces. As a result, they sometimes seem a bit sluggish, which is why many models can work much faster in the absence of people (ensured by a fence or sensor monitoring).

Lightweight robots and cobots are certainly the most sensible solution to be used in small businesses, laboratories, or in trade. In addition to the low price, it is above all the ease of use and programming that makes cobots so attractive.

Also Read: The Artificial Intelligence In Three phases To Autonomous IT

What Robots Can & Cannot do, The Areas of Application

Of course, some jobs would not be possible without intelligent mobile robots. Especially in extreme environments such as deep-sea research or space travel, researchers rely on the support of robots. Technically demanding tasks are also not an issue for autonomous systems.

Then there are the uses in industrial mass production. Here, the six-axis, Scara, or Delta systems are primarily responsible for efficiency; they are supposed to weld, paint, load machines, and process workpieces. Fast and around the clock.
But what about small businesses and craftsmen? Artisans, in particular, are often proud to do their work themselves, with their own hands. It shows their ingenuity, their craftsmanship. That is a good thing, yet there is one or the other task that justifies using an industrial robot or cobot. Of course, even systems equipped with AI and machine learning cannot replace the experience and creativity of a master craftsman or skilled worker. Artificial intelligence is far from being that good. But jobs that are stupidly repetitive, physically difficult, or dirty and dusty, robots can do very well. And let’s be honest: there is work like this everywhere. If you’ve never thought about a steel or aluminum colleague before, maybe you should. It could change your work life. Positive.

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What is AI?

Artificial Intelligence is a branch of a computer system that works and acts the way humans do. Eg: SIRI, Alexa, facial recognition, self-driving cars, which “pop” up for us when we think of AI.

When AI Takes Over

Artificial Intelligence is replacing high-level, judgment-based skill sets. Many repetitive tasks will be replaced by robots in the future. This leaves people doubting about future jobs.

Why should you re-skill and retrain?

New technologies can unleash their potential in a short time. Hence, private organizations and government sectors need to create concrete plans to re-engineer the workforce and identify effective ways to ensure a smooth transition for their employees in the short term when automation has the most effect. Some of the reasons to re-skill are as follows:

1. Key to win AI talent war.
2. Save organizations from spending a lot of money on hiring the right talent.
3. Can lead to higher performance by employees.
4. It will help to reintegrate into the workplace.

Strategies to Reskill Your Employees

When looking to integrate new technologies like AI in your product, it is important to ensure that you are not replacing the employees, rather upskill them with necessary courses, tools.

Here are three skills to ensure your collaboration with employees and AI.

1. Once you Identify the skills to work on, Anticipate the new types of skills that are needed to do that job, and hence, ask your employees to upskill with some of the artificial intelligence courses available across the internet so that your employees are ready when the time comes.

1. Laying off your old employees and hiring new ones is a costly process. Instead, keep your current employees, as they are familiar with your company’s product, growth, customers.

1. Your business needs to refine your employee’s skills. Training, development, and deployment can take a long time, however, take time and work with your employees to enhance your company’s product.

3 Ways To Reskill Your Workforce

To drive toward organizational goals, employees must adapt to the workplace changes.

What Is Reskilling?

Reskilling is all about developing a new skill. Often reskilling allows employees to take on a new role within an organization.

Reskilling: If your company has business analysts, data analysts or data engineering, then they could be good candidates to train for AI tasks. This includes focusing on skills like Python, R, NLP, and TensorFlow, which is a deep learning framework. Many educators like Great Learning, Coursera, and Udemy provide Artificial intelligence certifications online for upskilling. This not only helps employees to develop AIML skills but also gets value for the company with existing experience in AI to be a leader and mentor for developing employees.
Here are 3 ways to effectively reskill your workforce:

1. Evaluate And Strategize

Evaluating your learning strategy starts with an honest assessment.

• What is your current learning strategy that helps your organization?
• Have you been hitting these goals?
• Do you have any existing skills gaps?

After assessing your current state of learning, you need to determine whether these goals are still relevant.

• Are different behaviors or skills required for your people to perform their jobs effectively?
• Which skills will drive your business forward?
• Check if your sales and marketing team has transitioned from in-person to virtual sales?
• Check if you are still on in-shopping or home delivery?

These types of changes are necessary for learning strategy.

2. Get Started And Be Agile

Once your new learning strategy is ready, it’s time to get started! Implement, test, and then improve based on what you’ve learned. In doing so, you’ll quickly identify skills that are necessary for the growth of your business.
However, reskilling can not only help your organization to face a new challenge but also close current critical talent gaps but will better prepare you to master future disruptions as well.

3. Moving Forward

Newmarket crises constantly force us to evaluate our learning programs. COVID-19 has sought to drive its organization forward in reskilling. We must set sound strategies, test our learnings, and maintain our investment if we want to find success.

Organizations are re-skilling their workforce.

Leading organizations are creating programs to retrain their employees. One way to upskill workers is to allow individuals to learn new skills from different tools. During COVID, many of the learning platforms came forward to give out their resources for free to help professionals enhance their skills, and Great Learning is an excellent example. These platforms offer free training for both professionals and fresh aspirants to upskill themselves. Also, organizations can take advantage of these platforms and ask their employees to reskill.

Why is Continuous Learning Via Online Training Programs Crucial?

According to experts, AIML needs continuous learning!
One needs to learn languages such as Python, R, and the fundamentals of Statistics and Machine Learning concepts. Then, to take it to the next level, one needs to do enhancement with advanced concepts of Machine learning algorithms, NLP, neural networks, etc. All of this requires extensive training from top online educations, but most companies may not provide it.

Yet, some organizations like Wipro are working to ensure that skills gaps with employees and run multiple initiatives aimed at reskilling employees.

Here I would like to add up few courses that might help your employees to re-skill

1. Post Graduate Certification In Artificial Intelligence and Machine Learning- By Great Learning in collaboration with Texas McCombs.
2. Applied Machine Learning Course- By Applied AI Course.
3. Post Graduate Course in Machine Learning and AI – By Amity Online.
4. Artificial Intelligence Course For Leaders- By Great Learning in collaboration with Texas McCombs.
5. Columbia University’s Artificial Intelligence Course – By Pearson Professional Programs.
6. Artificial Intelligence – By Kellogg School of Management.
7. Machine Learning: Fundamentals and Algorithms – By Carnegie Mellon University.
8. Machine Learning AI Certification by Stanford University- By Coursera.
9. The Business of AI- By London Business School.
10. 10.  Learn AI from ML experts at Google – By Google

Summary

Undoubtedly, AI is a huge asset to all processes and is going to disrupt many industries and economies. Hence, it is costly to look and hire new AI experts. Instead, make strategies to help reskill your employees for a better understanding of the technology. No change comes without results, but I am certain that we will find solutions to reskill employees with AI and other emerging technologies. With new learning, we can decrease this disruption and create a better future of work.

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Artificial intelligence is nothing new

Maybe you’ll be disappointed…
But artificial intelligence originated in the 1950s by the American computer scientist and researcher John McCarthy. Who then described artificial intelligence as a machine that can think just like a human. Since then, the development has continued with some “notches” in the curve for interest in the area.

But in the mid-90s, for example, Deep Blue became the first chess-playing computer system (IBM) to beat world chess champion, Garry Kasparov.

Why is so much happening around AI now?

One factor that affects the increased interest in artificial intelligence is that today there is great access to large amounts of data. It facilitates the creation of various AI solutions. Other factors that affect interest and development are:

– Cheap cloud services make it possible to create AI solutions at a reasonable cost.

–  Today there are frameworks and tools that simplify the work of developing AI solutions.

–  Computer power in the form of fast processors adapted to AI.

–  A lot of interest in AI breeds even more interest in AI and in developing new AI solutions.

Areas of application for artificial intelligence

Nothing is new under the sun, it’s usually hot. What makes AI more difficult today is that there are differences from “before”.

OCR (optical character recognition), ie text interpretation, is no longer perceived as an example of “artificial intelligence”, as it has become a routine technology for a long time.

The difference today compared to before is that current OCR applications integrated with AI technology, provide enormously improved accuracy and speed because it uses machine learning technology.

You can divide the area of ​​artificial Intelligence into different areas of application. A few examples are:
Reasoning functions
In this area, there are various solutions for data analysis (data science) for forecasting and probability-based solutions.

An example is being able to predict how many items of a certain type need to be purchased for a company or warehouse. Where the data is based on historical information or real-time information from various sources.

Examples of how AI can be used

AI and machine learning are around you today in real-time.

Just take, for example, Facebook’s face recognition, the best way suggestions in Google Maps, or the personalized recommendations on Amazon (more on these later in the text).

CRM with AI

An important parameter to follow up for all companies is customer loss (Customer Churn Rate). This is due to the fact that it is cheaper to retain current customers than to acquire new ones. Loss of customers is simply a lost value for the company. In this area, AI can be used to predict which customers are considering leaving the company so that they can be contacted.

Do you want more areas where AI is used in CRM? Take a look at the video below about Salesforce Einstein, an AI tool that helps companies get a data-driven sales culture.

Many manufacturing companies already collect large amounts of data from various plant sensors during its production. Information that is a perfect basis for AI. Where the information can be used for fault detection and quality control without human intervention.

Another area in production where AI is used is planning and schedule optimization. By being able to quickly predict when different machines are available, it leads to more efficient and optimized manufacturing. In the video below you can see how BMW uses AI to handle deviations in real-time. 

AI in service and aftermarket

AI is an excellent tool for preventive maintenance, needs planning, and aftermarket activities. That is, to continuously check when parts in a machine or engine must be replaced, even before something has broken.

AI in e-commerce

AI can be used to individually customize e-commerce and web pages. Using algorithms, AI can predict what each customer and visitor wants and display the most relevant products and recommendations automatically. 

We are also changing the way we consume content on, for example, an e-commerce site. Today we search by writing. But more and more people are being introduced to search using pictures, videos, or speech.

AI to analyze Big Data 

With analysis of large amounts, for example, traffic data from websites, e-mail, or other network data.

All to find deviations from the normal that involve some form of security threat. Where the changes take place so quickly and the amounts of data are so enormous from both own and other sources that it is difficult to draw the right conclusions. There is AI logic that gives you information about what changes you should act on.

AI in economics and finance 

Few areas are better suited for AI and machine learning than the economics area. This is in view of the fact that these are often large volumes of data. Today, AI and algorithms are used in stock trading, lending, and insurance to assess risks. But also in follow-up and analysis. 

Benefits of AI

Decision

AI and machine learning algorithms can prioritize and automate your decisions. They can also alert you to immediate action. AI can also process both historical data and input data in real-time. Which means you can react to what’s happening right now.

Analysis and insight

AI can analyze large, complex amounts of data and from these reach its own insights in a way and at a speed that is beyond our human ability.

Efficiency

With AI, the company’s efficiency can be significantly improved in, for example, scheduling and planning, automation of tasks, or quality control.

The post What Can Artificial Intelligence Do In Your Business? appeared first on TechReviewsCorner.

It happens that whenever we talk about the Internet of Things, we paint a future full of these solutions, of smart cities in which pedestrians gain ground over vehicles, with smart buildings, and much more. We forget that, on many occasions, IoT solutions will go unnoticed by the majority, as is the case with IIoT, the Industrial Internet of Things.

The IIoT focuses exclusively on industrial applications, such as chain production, manufacturing, or processes in the agri-food industry. In other words, we remove all products for domestic applications from the connected equation and focus on increasing process efficiency, health, and safety.

How can the industry take advantage of this technology? What strategies are the most suitable for its adoption?

IIoT adoption strategies

First of all, a little clarification: IIoT has existed in industries for more than half a century. Since then, industries, factories, and factories have sensors connected to computers, dedicated to collecting data and recording it for analysis. That is the basic principle of the IIoT, and also of the IoT. Today, with other technologies combined and with better sensors, we can go further (and we coined an acronym), but the basis of everything is older.

Consider IIoT at the edge

In the past, adding a single sensor could be cost-prohibitive. The normal thing was to use sensors and actuators in specific areas, related to critical safety equipment, for example. Today, the reduced costs of new technologies allow IIoT to be applied on the periphery, where it was not profitable before.

The IIoT has benefited from low-cost sensors and cost savings of ownership as many of its workloads migrate to the cloud. In addition, it is much easier to analyze the data thanks to machine learning.

Use hidden sensors to reduce costs and control downtime

The low cost of today’s sensors invites them to be used in places that were traditionally supervised by human operators, such as a packaging or assembly line. If a small glitch occurred in those chains that stopped the process for, say, 15 seconds, manually recording that glitch could be a hassle (and not done).

By introducing low-cost sensors that can help monitor these small, almost unnoticed failures, we will gain information that will allow us to optimize those processes. The profitability, in those cases, is enormous given the low investment in components.

Going for the Cloud to reduce equipment ownership costs

Among the many benefits of the cloud is undoubtedly cost savings. We’re not just talking about the initial deployment, but also the cost of infrastructure ownership. In this way, companies can capture IIoT data without the need to employ specialists or invest in expensive on-site equipment.

No need to implement a complete IIoT strategy from the start

As in many cases, a progressive approach is often much more interesting and efficient than tackling a complete transformation in one step.

Also Read: MACHINE LEARNING FOR COMPANIES: ADVANTAGES OF ARTIFICIAL INTELLIGENCE FOR YOUR BUSINESS

The post Strategies For The Adoption Of IIoT In Industries appeared first on TechReviewsCorner.

Without a doubt, the most cutting-edge technological trend of recent years is everything related to artificial intelligence and data analysis. And it is that relatively recently there have been great advances in this field, which together with the availability of enormous amounts of data and increasing computing power are giving rise to all kinds of very interesting practical applications.

The problem comes when the terms related to the field become marketing empty words that in many cases are outright lies. It is very common to talk that this or that product uses artificial intelligence to achieve something and, sometimes, they are conventional algorithms making predictable decisions.

What is Artificial Intelligence?

Artificial intelligence (AI) was born as a science many years ago when the possibilities of computers were really limited, and it refers to making machines simulate the functions of the human brain.

AI is classified into two categories based on its capabilities:

• General (or strong) AI: that tries to achieve machines/software capable of having intelligence in the broadest sense of the word, in activities that involve understanding, thinking, and reasoning on general issues, on things that any human being can do.
• Narrow (or weak) AI: which focuses on providing intelligence to a machine/software within a very specific and closed area or for a very specific task.

Thus, for example, a strong AI would be able to learn by itself and without external intervention to play any board game that we “put before it”, while a weak AI would learn to play a specific game like chess or chess. Go. What’s more, a hypothetical strong AI would understand what the game is, what the objective is, and how to play it, while the weak AI, although it plays Go better than anyone else (a tremendously complicated game), will not really have a clue what it is doing.

One of the crucial questions when it comes to distinguishing an artificial intelligence system from mere traditional software (complex as it may be, which brings us to the jokes above) is that AI “programs” itself. That is, it does not consist of a series of predictable logical sequences, but rather they have the ability to generate logical reasoning, learning, and self-correction on their own.

The field has come a long way in these years and we have weak AIs capable of doing incredible things. Strong AIs remain a researcher’s dream and the basis of the scripts for many science fiction novels and films.

What is Machine Learning?

Machine Learning (ML) or machine learning is considered a subset of artificial intelligence. This is one of the ways we have to make machines learn and “think” like humans. As its name suggests, ML techniques are used when we want machines to learn from the information we provide them. It is analogous to how human babies learn: based on observation, trial, and error. They are provided with enough data so that they can learn a certain and limited task (remember: weak AI), and then they are able to apply that knowledge to new data, correcting themselves and learning more over time.

There are many ways to teach a machine to “learn”: supervised, unsupervised, semi-supervised, and reinforcement learning techniques, depending on whether the correct solution is given to the algorithm while it is learning, it is not given the solution, it is Sometimes you give or are only scored based on how well or poorly you do, respectively. And there are many algorithms that can be used for different types of problems: prediction, classification, regression, etc …

You may have heard of algorithms such as simple or polynomial linear regression, support vector machines, decision trees, Random Forest, K nearest neighbors … These are just some of the common algorithms used in ML. But there are many more.

But knowing these algorithms and what they are for (to train the model) is just one of the things that need to be known. Before it is also very important to learn how to obtain and load the data, do an exploratory analysis of the same, clean the information … The quality of the learning depends on the quality of the data, or as they say in ML: “Garbage enters, garbage comes out”.

Today, the Machine Learning libraries for Python and R have evolved a lot, so even a developer with no knowledge of mathematics or statistics beyond that of the institute, can build, train, test, deploy and use ML models for applications of the real world. Although it is very important to know all the processes well and understand how all these algorithms work to make good decisions when selecting the most appropriate for each problem.

What is Deep Learning?

Within Machine Learning there is a branch called Deep Learning (DL) that has a different approach when creating machine learning. Their techniques are based on the use of what are called artificial neural networks. The “deep” refers to the fact that current techniques are capable of creating networks of many neural layers deep, achieving unthinkable results a little more than a decade ago, since great advances have been made since 2010, together with large improvements in computing power.

In recent years Deep Learning has been applied with overwhelming success to activities related to speech recognition, language processing, computer vision, machine translation, content filtering, medical image analysis, bioinformatics, drug design … obtaining results equal to or better than those of human experts in the field of application. Although you don’t have to go to such specialized things to see it in action: from Netflix recommendations to your interactions with your voice assistant (Alexa, Siri, or Google assistant) to mobile applications that change your face … They all use Deep Learning to function.

In general, it is often said (take it with a grain of salt) that if the information you have is relatively little and the number of variables that come into play is relatively small, general ML techniques are best suited to solve the problem. But if you have huge amounts of data to train the network and there are thousands of variables involved, then Deep Learning is the way to go. Now, you must bear in mind that the DL is more difficult to implement, it takes more time to train the models and it needs much more computing power (they usually “pull” GPUs, graphics processors optimized for this task), but the problems are usually more complex as well.

What is Big Data?

The concept of Big data is much easier to understand. In simple words, this discipline groups the techniques necessary to capture, store, homogenize, transfer, consult, visualize, and analyze data on a large scale and in a systematic way.

Think, for example, of the data from thousands of sensors in a country’s electrical network that send data every second to be analyzed, or the information generated by a social network such as Facebook or Twitter with hundreds (or thousands) of millions of users. We are talking about huge and continuous volumes that are not suitable for use with traditional data processing systems, such as SQL databases or SPSS-style statistics packages.

Big Data is traditionally characterized by 3 V:

• The high volume of information. For example, Facebook has 2 billion users and Twitter about 400 million, who are constantly providing information to these social networks in very high volumes, and it is necessary to store and manage it.
• Speed: following the example of social networks, every day Facebook collects around 1 billion photos and Twitter manages more than 500 million tweets, not counting likes and many other data. Big Data deals with that speed data receiving and processing so that it can flow and be processed properly without bottlenecks.
• Variety: the infinity of different types of data can be received, some structured (such as a sensor reading, or alike ) and others unstructured (such as an image, the content of a tweet, or a voice recording). Big Data techniques must deal with all of them, manage, classify, and homogenize them.

Another of the great challenges associated with the collection of this type of massive information has to do with the privacy and security of said information, as well as the quality of the data to avoid biases of all kinds.

As you can see, the techniques and knowledge necessary to do Big Data have nothing to do with those required for AI, ML, or DL, although the term is often used very lightly.

These data can feed the algorithms used in the previous techniques, that is, they can be the source of information from which specialized models of Machine Learning or Deep Learning are fed. But they can also be used in other ways, which leads us to …

What is Data Science?

When we talk about data science, we refer in many cases to the extraction of relevant information from data sets, also called KDD ( Knowledge Discovery in Databases, knowledge discovery in databases). It uses various techniques from many fields: mathematics, programming, statistical modeling, data visualization, pattern recognition, and learning, uncertainty modeling, data storage, and cloud computing.

Data science can also refer, more broadly,  to the methods, processes, and systems that involve data processing for this extraction of knowledge. It can include statistical techniques and data analysis to intelligent models that learn “by themselves” (unsupervised), which would also be part of Machine Learning. In fact, this term can be confused with data mining  (more fashionable a few years ago) or with Machine Learning itself.

Data science experts (often called data scientists ) focus on solving problems involving complex data, looking for patterns in the information, relevant correlations, and ultimately, gaining insight from the data. They are usually experts in math, statistics, and programming (although they don’t have to be experts in all three).

Unlike experts in Artificial Intelligence (or Machine Learning or Deep Learning ), who seek to generalize the solution to problems through machine learning, data scientists generate particular and specific knowledge from the data from which they start. Which is a substantial difference in approach, and in the knowledge and techniques required for each specialization.

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