An artificial intelligence certification can accelerate your career in 2022

Posted on November 21, 2022March 26, 2024 by Imarticus Learning

An artificial intelligence certification can accelerate your career in 2022

If you are looking for a career option that is far-sighted, one that has a great future scope, then the answer is artificial intelligence. The demand for it will grow with the technological advancements in the field. As we are encountering a global shift towards digitalisation and web 3.0, the developments are increasing each day. Thus, there is more scope for a career in artificial intelligence in the coming future.

To make way for yourself in a competitive and ideal field like this is quite difficult but an AI certification can help you with this. An IIT AI ML course will not only provide you with the best quality of education but also validate your skills with the certification. And nowadays, it is one of the important factors when you apply to a company for a job position.

Before jumping on any further let us look at some statistics about the artificial intelligence market. As per a report published in fortune business insights, the projected market size of Ai is sighted to be USD 1394.30 billion in 2029 at a CAGR of 20.1% from USD 387.45 billion in 2022. This report not only speaks about the figure but also gives us insights into the growing application of AI in several fields like retail, healthcare, automotive, and many more, all of which are contributing to the growth of the global market.

All this makes it quite clear that artificial intelligence is the future, and one must start now with an AI certification or machine learning course to be an asset in the future. Now, let us understand AI and how its certification can speed up one’s career.

What is Artificial Intelligence?

AI is the technological process of programming that enables a computer to decide for itself. It develops computer programs because of which the machine is capable of doing certain tasks and solving problems. To some extent, it can also be called a replacement for human intelligence. However, it also has its limitations.

An AI can do various things like speech recognition, visual perception, word translation, and more or less everything that needs human intelligence.

Under artificial intelligence comes machine learning which teaches computers to learn from data. The process of this is quite extensive as a lot of data is fed to the computer to identify its patterns by itself.

How does Artificial Intelligence Work?

Till now, we have understood a little about what AI is, and here comes the most complicated part as we will unfold the layers of function in artificial intelligence. As we already know it has various applications in many fields that boost the performance of that particular sphere.

Now let us dig deep into several concepts that come under AI, which are important if you are thinking to have a career in it.

Machine Learning- This branch of artificial intelligence enables software applications to conclude from studying data without even being programmed to do so. This provides businesses with useful insights into consumer behaviour. It has a wide range of applications and one can learn about this in depth through a machine learning course.
Cognitive Computing- It uses a mixture of AI, machine learning, and natural language processing to solve problems as a human would do. It mimics human intelligence by analysing various factors. However, its end goal is to assist humans in decision-making.
Deep Learning- This is another subset of machine learning that deals with large data. It makes the computer model perform tasks from representation learning artificial neural networks. For example, driverless cars, and hand-free speakers.

Apart from these, there are various other concepts that one can learn about from an IIT AI ML course. Now, we must look into the future of AI and what it holds for us. It will make your dilemma clear about having a career in AI.

Future of Artificial Intelligence

AI has already entered our lives and transformed them to be much better. However, the future awaits many more such AI applications that will just amaze humanity. Artificial Intelligence will continue to improve efficiency and productivity in whichever sector it is used in. It is the upcoming future that will rule in every industry because of its use and the way it makes things easier. In simple words, now is the perfect time to gain AI certification and build yourself for the coming future that holds so much for you. You can also be a part of this revolution in which AI has an unparalleled role to play.

Conclusion

Till now, you have a brief knowledge of artificial intelligence and how choosing it as a career option can take you toward your success. However, a lot is there to understand and learn about it in depth. To do so, you must opt for a certification course in artificial intelligence and machine learning. It will be a cherry on the top if you get to learn from an IIT AI ML course, that will turn you into the best professional anyone can ask for.

Why are companies greatly demanding candidates with a artificial intelligence certification?

Posted on September 11, 2022March 29, 2024 by Imarticus Learning

Artificial Intelligence has expanded at an exponential rate in recent years, despite significant progress in the field. In the field of computer science, AI practices can be found everywhere. It provides you with an idea of how many different ways a computer system can be designed.

It is designed to carry out the cognitive functions that humans have specified. This indicates that the scope of an artificial intelligence course is enormous, and AI has potential that is currently beyond human grasp.

Scope of An Artificial Intelligence Course in India

Artificial Intelligence has enormous potential to transform every sector of the economy for the greater good.

AI encompasses a wide range of technologies, including self-improving algorithms, machine learning, big data, and pattern recognition, to name a few. There will be few industries or sectors left unaffected by this potent weapon in the not too distant future. This is why online Artificial Intelligence courses are becoming increasingly popular in India.

With each passing day, the gap between the number of AI professionals required and those available widens. Corporations are spending money to train their existing employees on Artificial Intelligence technologies. However, the demand is far higher.

Learn AI

Certification In Artificial Intelligence & Machine Learning

Learn AI via 25 in-class, real-world projects focused on offering exposure to various industries. This 9-month program will help you prepare for the roles of Data Scientist, Data Analyst, Machine Learning Engineer, and AI Engineer.

This machine learning certification program was established in collaboration with the E&ICT Academy, IIT Guwahati, and industry professionals to give an optimum learning outcome

This course will strengthen your core abilities, allow you to take advantage of our Expert Mentorship program, and give you a practical grasp of AI and Machine Learning.

Data Science Prodegree

Develop your knowledge of Data Science ideas and build robust models to generate relevant business insights or forecasts with a working knowledge of critical Data Analytics technologies such as Python, R, SQL, and Tableau in these 14 in-class and industry-oriented projects.

Take Away

AI is one of the most popular technologies on the planet because of its diversity and superior solutions. It has been rapidly expanding. As you can see, the scope of AI has broadened to include a wide range of industries, including healthcare, transportation, etc., security, etc. Multiple industries require the expertise of experienced AI specialists as a result of this increase.

Check out Imarticus IT classes, targeted at working professionals, if you want to learn more about AI and machine learning algorithms.

How Much Do AI Researchers Make?

Posted on January 30, 2020February 24, 2023 by Imarticus Learning

What is Artificial Intelligence?

Also known as Machine Intelligence, Artificial Intelligence deals with the automation of machines so that they can perform human-like activities. Artificial Intelligence is being used in a lot of data-oriented industries such as insurance, healthcare, retail, technology, automotive, etc.

It also finds use in the finance sectors where various credit frauds and identity thefts need to be traced. Artificial Intelligence makes use of various machine learning algorithms to perform a specific set of tasks. Artificial Intelligence training is shaping our new world. It aims at achieving a specific goal by rationalizing the processes involved and then taking actions accordingly.

Who are AI-Researchers?

AI Researchers are those who apply the fundamentals of artificial intelligence to various data sets to draw out conclusions and various business insights. The job of AI Researchers includes language processing, algorithm building, development of data sorting mechanisms, etc. Also, it includes the movement and manipulation of data from various channels so that an efficient and effective transformation of data takes place with the utmost ease and efficiency.

AI researchers usually have a great understanding of various computer programming languages, hence making them proficient in what they do. Their expertise lies in building machines which reduces the human effort to a great extent. Artificial Intelligence researchers are in great demand owning it to the fast-paced technological environment and this ever-growing need for constant change. Thus, making AI Research a lucrative career path. People with less experience are also trying their hands in this area, given the financial attractiveness of this field.

How much do AI Researchers make?

Artificial Intelligence has become one of the most important elements of this data-oriented world. Companies are moving towards automation, making the best use of artificial intelligence and its ability to analyze, compile and assess huge data within a given time frame. The application of artificial intelligence requires expertise, thus demanding a pedestal in the corporate world. And this pedestal converts to a huge salary when converted into financial terms.

Artificial Intelligence is an area with a high demand for skilled personnel and less supply of the same, making it a really expensive affair for the companies looking forward to hiring AI Researchers and experts. The pay is the sweet spot for AI Researchers as the job role comes with huge money. The average salary for an AI Researcher in Silicon Valley is somewhere around $100,000 and $150,000 as it involves a lot of brainstorming and this pay is further increasing with the increase in applications of artificial intelligence.

Also, with increasing applications of Artificial Intelligence, the complexity of the operations is increasing, thus making the job of an AI Researcher a hotspot.

AI training and commands have such huge salaries as it provides a practical approach and puts the theoretical knowledge to some good use. Also, one can teach oneself and be ready for the market but understanding AI takes sufficient time and is not a cakewalk.

It is rapidly growing and the ones who are catching up with this growth are being rewarded in the form of really high salaries which keep them motivated to stay on the path as this growth graph of Artificial Intelligence is constantly going up and will not become flat anytime soon. As per Glassdoor, the average base pay of an AI Researcher is $111,118 per year which is pretty high when compared to other sectors of the economy.

Conclusion

Artificial Intelligence has made the world more dynamic than it was ever before. It is evolving at a very fast pace thus giving rise to a huge demand for AI professionals and the salary associated with the field is making it even more attractive for the generations to come.

We at Imarticus Learning offer Analytics and Artificial Intelligence courses at our centers in Mumbai, Thane, Pune, Bangalore, Chennai, Delhi, Gurgaon, Noida, Ahmedabad, and Jaipur.

What Are Important Ways That AI Is Helping E-Commerce Stores?

Posted on October 11, 2019June 15, 2022 by Imarticus Learning

The Ecommerce Industry

The e-commerce industry has proved to be a boon for all the shopaholics who are too lethargic for a regular brick and motor engagement. Growing in double digits the expansion in the e-commerce industry is unmatched by any other and with the potential to grow multiple folds in the coming years it has set new highs.

In a broad sense of things, the concept behind the e-commerce world is simple, creating on the online market place with multiple stores available to shop anytime using the means of smartphones and other computerized devices that support web surfing.

The virtual market is not bounded by geography, having its customer base all across the world. What’s different about this shopping escapade is that it makes the entire store available for you to facilitate your shopping spree, all with a few clicks. I wonder how many times it happens that I am not sure about what exactly I need to purchase unless acquainted with the varieties available.

Now if we have to walk by several stores to find out what could be bought it will be tiresome, to say the least. Let’s assume that we somehow managed to step into each of them, how will we compare all the available products in real-time? That’s where the e-commerce industry adds value and steals the show with convenience.

The e-commerce stores not only help to bring everything together but also helps to search select and choose by providing valuable suggestions and insightful product descriptions. It also lets you read into the feedback provided by the users of the products that might help you buy better.

In the tangible world, we have a shop for every need, we have shopping complexes for multiple segments. This evolution went a little further in the era of the internet with e-commerce where we have all the product segments from all the known brands under a few keystrokes.

AI applications in the e-commerce industry

While shopping at stores with a physical address on the map, what attracts the most apart from quality goodies is the presentation and organization of the products.

Similarly when buying goods online what helps increase engagement and purchase? The answer is better to search for tools and classified product segments. This is where AI fits into the e-commerce must-have tools.

The high-tech AI-enabled solutions can also help in searching product descriptions and other relevant details to form a variety of keywords that might match the user’s search and help discover the product better. This doesn’t stop here, the AI-powered solutions also help with product selection by asking some intelligent questions and narrowing down the list for us.

At times it so happens that we know what we are looking for but the name is unknown to us and thus we feed in a variety of keywords to complete our search. The predictive search mechanism provided by Artificial Intelligence training uses the past search and purchases history helping us identify what we might be looking for with relative ease saving a lot of time and keystroke efforts.

Arrangement of products and tidiness are some of the key drivers of customers in the traditional brick and motors store, how do you implicate this approach online? Well, the answer doesn’t require a brainstorming session, it is through the website design.

Making the website aesthetic needs a well-planned web design that not only looks good but also goes along with the objective of the website. From optimized website design testing to improving decisions with auto traffic analysis & better sales funnel structuring, AI delivers on all aspects of customer conversions and engagement.

In present-day scenario conversational chatbots are mainstream for better customer servicing, it could also be seen as a norm, whatever site you visit for your purchase you are bound to be greeted by a bot. This evolution has propelled further with a new wave of intelligent sales chatbot. This new AI by-product is hyper-personal in their functioning, providing customized recommendations and suggestions for better conversion.

Conclusion

AI has improved the e-commerce industry to a great extent by providing better search options for product searches to suggesting an optimized website layout for better conversions. Apart from the mainstream chatbots for customer servicing this new AI wave has welcomed the trendy sales chatbot that uses customer preferences data for good by providing customized and hyper-personal shopping experience.

What Are The Important Ways That AI Is Helping E-Commerce Stores?

Posted on September 25, 2019June 15, 2022 by Imarticus Learning

What Are The Important Ways That AI Is Helping E-Commerce Stores?

In a broad sense of things, the concept behind the e-commerce world is simple, creating on an online marketplace with multiple stores available to shop anytime using the means of smartphones and other computerized devices that support web surfing.

AI applications in the e-commerce industry

While shopping at stores with a physical address on the map, what attracts the most apart from quality goodies is the presentation and organization of the products.

The high-tech tech AI-enabled solutions can also help in searching product descriptions and other relevant details to form a variety of keywords that might match with the user’s search and help discover the product better. This doesn’t stop here, the AI-powered solutions also help with product selection by asking some intelligent questions and narrowing down the list for us.

At times it so happens that we know what we are looking for but the name is unknown to us and thus we feed in a variety of keywords to complete our search. The predictive search mechanism provided by AI technology uses our past search and purchase history helping us identify what we might be looking for with relative ease saving a lot of time and keystroke efforts.

Arrangement of products and tidiness are some of the key drivers of customers in the traditional brick motors store, how do you implicate this approach online? Well, the answer doesn’t require a brainstorming session, it is through the website design.

Making the website aesthetic needs a well-planned web design that not only looks good but also goes along with the objective of the website. From optimized website design testing to improving decisions with auto traffic analysis & better sales funnel structuring, Artificial Intelligence Training delivers on all aspects of customer conversions and engagement.

In the present-day scenario conversational chatbots are mainstream for better customer servicing, it could also be seen as a norm, whatever site you visit for your purchase you are bound to be greeted by a bot. This evolution has been propelled further by a new wave of intelligent sales chatbots. This new AI by-product is hyper-personal in its functioning, providing customized recommendations and suggestions for better conversion.

Conclusion

A Look at the 3 Most Common Machine Learning Obstacles

Posted on September 23, 2019June 22, 2022 by Imarticus Learning

A Look at the 3 Most Common Machine Learning Obstacles

When we talk about artificial intelligence (AI), the research and its findings have surpassed our little expectations. Some experts also believe that this is the golden age of AI and machine learning (ML) projects where the human mind is still surprised at all the possibilities that they bring to the table. However, it is only when you start working on a project involving these advanced technologies that you realize that there are a few obstacles that you need to address before you can start throwing a party.

Predictive assembly line maintenance, image recognition, and automatic disease detection are some of the biggest applications of ML-driven automation. But what are the hurdles that data scientists need to cross if they want to practically execute these applications and gain the desired outcome?

This article will give you an overview of the three common obstacles involved in machine learning models.

Common Machine Learning Obstacles

On theory, machine learning evangelists tend to liken the technology to magic. People scrolling through Facebook watch videos that use buzzwords in their captions and believe that AI can do wonders. Of course, it can, but when you think practically it is not as easy as it sounds. Commercial use of machine learning still has a long way to go because the reference dataset that is essential for any such model to function needs to be tidied up and organized at such a minute level it becomes tedious.

Ask any data scientist who has worked in a deep learning project and she will tell you all about it: the time, the resources, and the particular skills needed to create the database, sometimes known as a training set. But these are challenges found in any project. When you deal with machine learning, there are a few peculiar ones too.

Let’s dig deeper into these three common obstacles and find out why they are so integral to the larger machine learning problem.

The Problem of Black Box

Imagine a machine learning training program that is developed to predict if a given object is a red apple or not. During the early days of machine learning research, this meant writing a simple program with elements that involved the color red and the shape of an apple. Essentially, such a program was created through a thorough understanding of those who developed it. The problem with modern programs is that although humans developed it, they have no idea how the program actually detects the object, and that too with unprecedented accuracy. This is also one of the issues hampering the wide application of data classification models.

Experts have studied this problem and tried to crack it, but the solution still seems elusive because there is absolutely no way to get into the process while it is running. Although the program gives out fabulous results – results that are much needed to detect if a given fruit is a red apple or not from a wide range of fruits that also include non-apples – but the lack of knowledge as to how it works makes the whole science behind it feel like alchemy.

If you have been following world news related to AI-enabled products, this is probably the biggest cause of ‘accidents.’ That self-driving car hit a divider when there was no reason for it to hit it? That’s the black box problem right there.

What Classification Model to Choose?

This is another common obstacle that comes in the way of data scientists and successful AI tools. As you might know, each classification model has its own set of pros and cons. There is also the issue of the unique set of data that has been fed to it and the unique outcome that is desired.

For example, a program wanting to detect a fruit as red apple is totally different from another program that requires the observation to be classified into two different possibilities. This puts the scientists behind the program in a difficult situation.

Although there are ways to simplify this to an extent, it often ends up as a process of trial-and-error. What needs to be accomplished, what is the type and volume of data, and what characteristics are involved are some of the common questions that need to be asked. Answers to these will help a team of engineers and data scientists selects an appropriate model. Some of these popular models are a k-nearest neighbor (kNN), decision trees, and logistic regression.

Data Overfitting

Understanding this will be easier because it can be described using an example. Take, for instance, a robot who has been fed the floor plan of a Walmart store. It is the only thing that has been fed to it, and the expected outcome is that the robot can successfully follow a set of directions and reach any given point in the store. What will happen if the robot instead is brought to a Costco store that is built entirely differently? Assumption tells us that it won’t be able to go beyond the initial steps as the floor plan in its memory and the floor plan of this new store do not match.

A variation of this fallacy is what is known as data overfitting in machine learning. A model is unable to generalize well to a set of new data. There are easy solutions to this, but experts suggest prevention rather than cure. Data regularization is one of those prevention mechanisms where a model is fed data sufficient for the requests that it will handle.

The above-mentioned three obstacles are the most common, but there are many more like talent deficit, unavailability of free data, and insufficiency of research and development in the field. In that vein, it is not fair of us to demand a lot more of the technology when it is relatively new compared to the technologies that took years and decades to evolve and are part of our routine use (internet protocol, hard disks, and GPS are some examples).

If you are an aspiring data scientist, the one thing that you can do is contribute to the research and development of machine learning and engage in more discussion both online and offline.

AI and Food: Safer and More Tasty Food?

Posted on September 3, 2019March 10, 2021 by Imarticus Learning

In February 2019, Tristan Greene wrote an article in The Next Web and quoted an IBM research study that suggested that artificial intelligence could improve the taste of food by creating new hybrid flavors. It took a part of the Internet by storm, less for its clickbait headline and more for its actuality. Greene was writing facts when he began his article with this: “AI will soon decide what we eat”.

Let’s explore the what, the why, and the how. We are sure you already know the why so we’ll mostly skip it.

Artificial Intelligence + Food. Really?

That seems to be a sensible question but not a surprising one. AI and machine learning have already taken over the world with them influencing everything from blockchain to computer vision to chemistry. So why not food production?

Now IBM, other tech giants, and new startups are changing that by feeding AI systems millions of different types of data in the areas of sensory science, consumer preference and flavor palettes to help generate new or advanced flavours that can literally put your mouth on fire. Or make it drool all day. Or make even the most tasteless food taste like heaven. Kale and quinoa, anyone?

The food industry has already scrambled to use artificial intelligence and machine learning for its sake. Take, for example, the world’s first automatic flatbread-making robot called Rotimatic which limits user control to just putting the ingredients into the appliance. It does all the dirty work by itself and claims to bake hot flatbread in under a minute.

Not just kitchen appliances, the food that we eat and its ingredients are also being influenced by AI and other techniques even as we debate whether genetically modified food products are safe for human consumption. Researches involving changes in the cooking style, omission or replacement of certain ingredients, and others have all been suggested by AI-driven tools. While none of them have hit the shelves yet, this new tool by IBM looks like it’s just around the corner.

According to the study, IBM and a company pioneering in flavors and food innovation named McCormick & Company created a novel AI system whose aim is to create new flavours. Published in February 2019, the blog post promised that some of its findings will be available on the shelf by the end of the year. While it is September and we still wait, let’s have a look at the scope of AI in the food industry.

How Does AI Help Food Become Better?

To answer this question, Greene uses the analogy of Google Analytics tools. Publicly available data like recipes, menus, and social media content about these recipes along with trends in the food industry are fed to AI systems. These then generate fresh, actionable insights.

An example is a tool that can show restaurants what the most popular food will be every month for the next 12 months. If this is a possible scenario, the restaurant can prepare itself and maybe even surprise its customers into submission, eventually becoming popular and running a successful service.

The same goes for farming models where new techniques are needed to plant and grow more produce as the population gets out of the window due to lack of space. Everyone involved in researches dealing with AI and the food industry is positive about what can be done.

Existing data is of prime importance if such tools are to bear any results. In the above example involving IBM, the tool is able to create new flavors because of the existence of data on different flavours that we currently have. In a way, AI is only helping us discover flavors sooner.

AI Everywhere in the Food Industry

Till now, we spoke about the use of AI in farming, food recipes, and restaurants. But what about food processing? Media suggests that AI is everywhere – from its help in sorting foods to making supermarkets more super.

According to a Food Industry Executive, there are a lot of examples that highlight the significance of AI in the food industry. Some of them are listed below, thanks to Krista Garver:

Food sorting – AI helps understand which potatoes (by their size and quality and age) should be made into French fries and which ones are suitable for hash browns or potato chips or some other food. This involves the usage of cameras and near-infrared sensors to study the geometry and quality of fruits and vegetables
Supply chain management – This is obvious: food monitoring, pricing and inventory management, and product tracking (from farms to supermarkets)
Hygiene – AI can detect if workers are wearing all the necessary equipment. Since AI tools are fed data about what constitutes 100% hygiene, they can constantly check the attire of workers and rate them on the basis of their current clothing. Is a worker not wearing a plastic hat? An alert goes to his manager
New products – This is similar to the IBM example seen above. Predictive algorithms can be used to understand what flavors are most popular in people of certain age groups. Why do kids love Kinder Joy? What is or are the ingredients that make them go bonkers?
Cleaning – This is the most promising one where ultrasonic sensing and optical fluorescence imaging can be used to detect bacteria in a utensil; this information can then be used to create a customized cleaning process for a batch of similar utensils.

Conclusion

It is mind-numbing (mouth-watering, too?) to visualize these products actually coming into form in a few years. Which is why there is no doubt that AI will revolutionize the food market. The only question that then remains: has the revolution already begun now that you can’t say no to a bunch of addictive products?

How Artificial Intelligence Help To Transform Employee Productivity?

Posted on August 26, 2019July 4, 2022 by Imarticus Learning

How does Artificial Intelligence Help To Transform Employee Productivity?

Every company moves towards becoming a tech company, and AI-enabled computers and bots will enter the field of how recruitment, on-boarding, training, and working happens at our workplaces. Here are just some of the areas where successful artificial intelligence courses used by tech companies to train smart machines could be used in the foreseeable future.

Onboarding and recruitments:

Did you know that many companies use AI-enabled systems to scan and identify the right person for the job in most smart tech companies? They can effectively and accurately wade through millions of applications and gain foresight from their profile sampling techniques to invite the deserving candidate.

Pymetrics uses neuroscience-inspired “games” to assess Artificial Intelligence Courses with emotional and cognitive features of the profile avoiding any human bias on gender, status, race or socioeconomic factors. They compare the new profiles to their inhouse data of profiles of persons who were successful at the job being recruited for. It can also make lateral options a choice for candidates who are not just right for the particular job but fit other open vacancies.

Similarly, Montage, with the top 100 amid Fortune 500 companies as clients use an AI-driven interviewing tool which can undertake automated scheduling, on-demand text interviewing and such to reduce unconscious biasing in recruitments.

Chatbots are not just for customer service and help the new recruits settle in better. Unabot, used by Unilever is a good example of using NLP (natural language processing) to answer queries on payroll and HR with advice to employees in plain and simple human language.

On-the-job training:

The entire learning process and training is full of examples of AI-interventions. They help garner from older experiences and transfer to new recruits the wealth of information required for being successful on the job. Honeywell uses AR/VR to capture the work experience and learn “lessons” from it to be passed on to new hires. Such tools keep records, use image recognition technology, play these back, provide real-time feedback, issue reminders, and help in a VR experience of the role.

Augmented workforce

Fears that AI will replace workers and take over their jobs, is baseless. The very aim of AI is to aid the workers and one should exploit the help in increasing productivity, efficiency and augmentation of the workforce since AI brings many benefits to its applications. Humans can better use their faculties for creative and human-interaction based areas of work in artificial intelligence courses since machines do need human interaction and maintenance too.

Machines have proven skills in repetitive tasks, providing insights into large volumes of data and the potential for predictive trend analysis. PeopleDoc, Betterworks and such can go a long way in bettering the day-to-day workplace experience with monitored processes and workflows and processes and RPA-robotic process automation.

Surveillance in the workplace

Are you aware that according to a Gartner survey, half the companies with 750million USD make gainful use digital data-gathering tools to monitor employee performance and activities? This includes employee engagement and satisfaction levels. Some companies use tracking devices to monitor bathroom breaks and audio analytics to determine voice stress levels. Others use the carrot of fitness and exercise programs through traceable Fitbits. Workplace Analytics is used by Humanyze on staff email and IM data, and microphone-equipped name badges. Not all AI is bad as bullying, stalking and security are good goals. Right?

Workplace Robots

Physical autonomous movement robots are fast becoming the means of access for warehousing and manufacturing installations. Robots like Segway have a delivery robot while, security robots like Gamma 2 keep the trespassers away, and ParkPlus helps you find parking slots. Include the automatic shuttles and driverless cars at workplaces and wonder why we humans are still complaining.

Conclusion:

Though the concepts have been around for ages the past two decades have seen a phenomenal and sustained increase in ML/AI applications. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence without the use of any human intervention or explicit programming. Machine learning is a subset of AI technology that develops complex algorithms based on mathematical models and data training to make predictions whenever new data is supplied to it for comparison.

Do you want to succeed in artificial intelligence courses? Then learn with Imarticus Learning for becoming career-ready and skilled. Why wait?

For more details in brief and for further career counseling, you can also contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi and Gurgaon.

How have statistical machines influenced Machine Learning?

Posted on June 6, 2019September 9, 2021 by Imarticus Learning

The past few years have witnessed tremendous growth of machine learning across various industries. From being a technology of the future, machine learning is now providing resources for billion-dollar businesses. One of the latest trend observed in this field is the application of statistical mechanics to process complex information. The areas where statistical mechanics is applied ranges from natural models of learning to cryptosystems and error correcting codes. This article discusses how has statistical mechanics influenced machine learning.
What is Statistical Mechanics?
Statistical mechanics is a prominent subject of the modern day’s physics. The fundamental study of any physical system with large numbers of degrees of freedom requires statistical mechanics. This approach makes use of probability theory, statistical methods and microscopic laws.
The statistical mechanics enables a better study of how macroscopic concepts such as temperature and pressure are related to the descriptions of the microscopic state which shifts around an average state. This helps us to connect the thermodynamic quantities such as heat capacity to the microscopic behavior. In classical thermodynamics, the only feasible option to do this is measure and tabulate all such quantities for each material.
Also, it can be used to study the systems that are in a non-equilibrium state. Statistical mechanics is often used for microscopically modeling the speed of irreversible processes. Chemical reactions or flows of particles and heat are examples of such processes.
So, How is it Influencing Machine Learning?
Anyone who has been following machine learning training would have heard about the backpropagation method used to train the neural networks. The main advantage of this method is the reduced loss functions and thereby improved accuracy. There is a relationship between the loss functions and many-dimensional space of the model’s coefficients. So, it is very beneficent to make the analogy to another many-dimensional minimization problem, potential energy minimization of the many-body physical system.
A statistical mechanical technique, called simulated annealing is used to find the energy minimum of a theoretical model for a condensed matter system. It involves simulating the motion of particles according to the physical laws with the temperature reducing from a higher to lower temperature gradually. With proper scheduling of the temperature reduction, we can settle the system into the lowest energy basin. In complex systems, it is often found that achieving global minimum every time is not possible. However, a more accurate value than that of the standard gradient descent method can be found.
Because of the similarities between the neural network loss functions and many-particle potential energy functions, simulated annealing has also been found to be applicable for training the artificial neural networks. Other many techniques used for minimizing artificial neural networks also use such analogies to physics. So basically, statistical mechanics and its techniques are being applied to improve machine learning, especially the deep learning algorithms.
If you find machine learning interesting and worth making a career out of it, join a machine learning course to know more about this. Also, in this time of data revolution, a machine learning certification can be very useful for your career prospects.

Tag: Artificial Intelligence Certification