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Tag: Artificial Intelligence

Ways To Use Artificial Intelligence In Education

Posted on by Imarticus Learning
Reading Time: 3 minutes

Ways To Use Artificial Intelligence In Education

Do you know that AI is very present in all our lives and has pervaded almost every space? Not just the imaginary humans with chips portrayed by fiction writers and science fiction movie makers but just look around.

Google searches, automatic sensors for reversing your car, automatic lens adjustments and light settings for those perfectly timed selfies, Google maps to take you straight to your destination, MRIs to detect those illnesses you never thought you had, multiple-choice answer sheets scored automatically on online learning sites, paying bills online, that favorite app you just downloaded and everything I between. They all run on the artificial intelligence courses of the self-learning algorithms of machine learning help make machines truly aid to human thinking through deep learning and neural networks.

Though AI has actually taken over most of the human tasks, they are still a long way off from replacing human beings and the one area where they have tremendous application potential is in education. Let’s reiterate that the basic aim of artificial intelligence courses and neural thinking is not to replace humans but to help them with repetitive tasks and data sifting far beyond the limits of the best human brains. So, in the future, AI and humanoid robots will not replace teachers. But they will transform how we learn, and what to learn and go a step further by helping us learn. That includes the teachers too who are constantly learning too!

Why AI matters in education:

Let us explore how AI is going to bring its benefits to the education experience of the future. The class sizes keep increasing with compulsory education and teachers are often facing many challenges in giving attention and help to the large numbers of students. A big challenge like this has been simplified by incorporating computer programs that allow each student to follow his own pace and learning curve. Individualized learning modules can help find knowledge gaps and personalize the learning materials to fill in the gaps.

Teachers can now get truly involved in teaching and rectifying the lacunae in the learning process. Besides, the teachers can also get recommendations on how to rectify the issues, what learning materials to use for personalizing the process and much more to help herd the students towards the right levels of comprehension and skills required. This could also be used for learning processes of differently challenged students.

The newer methods of experiential learning at educational institutions use advanced techniques of AI, machine learning and deep learning in instructing and teaching like chatbots and learning bots. A differentiated AI style of learning deals with the most effective style to help the student learn. Adaptive artificial intelligence courses based learning curates the learning exercises matching them to the student’s needs and knowledge gaps. Competency-based AI tests aid the students to gauge their learning levels and progress from thereon. Using all these three types of learning AI can test how well the students can adapt their learning to applications of it and thus promote the progress of students based on individual interests.

The benefits:

Some of the benefits of artificial intelligence courses that can be harnessed are: 

1. Grading, scoring, and such repetitive tasks can easily be handled by AI.

2. Personalization of educational software can be need-based and adapted to individual learning curves.

3. Lacunae and learning gaps can be predicted and rectified with suggestions for learning materials and courses needed to improve.

4. Tutoring through subject-specific learning bots, online self-paced courses etc can support students.

5. The feedback route is almost instantaneous and can be gainfully harnessed by both educators and learners.

6. AI has changed the way we search for and interact with data. Just Google for information on anything and everything is what 95% of the people do to find information.

7. AI will make teachers more effective and ever-learning educators.

8. AI will develop human skills and make trial-testing-and-error learning the norm.

9. Data harnessing and empowerment will change the learning experience using AI to find, support and teach students.

10. AI can offer both offline and online resources which will alter where we learn, how and who teaches them and help apply to learn to basic implicational skills.

Conclusions: 

What do you think would be the results of AI in education and the learning process? Yes, the education field is going to be very different from what we now see it as. Skills in learning applications will count for more. Jobs will be linked to skills and not degrees. Certification will emerge as a measurable tool of skills. And, if you want to explore more, why not do artificial intelligence courses at the reputed Imarticus Learning institute?

What Machine Learning Has To Do With Your Personal Finances?

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What Machine Learning Has To Do With Your Personal Finances?

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. Artificial intelligence is the ability of machines to simulate neural networks and human intelligence through machine learning courses without the use of any human intervention or explicit programming.

Though these two concepts that always go together have been around for ages, the past two decades have seen a phenomenal rise and exploitation of benefits of ML applications.

Let us explore some applications in real-life in the financial services area where they have made huge differences in customer service, fraud and risk management, and last but not least personal finance.

Examples in Customer Service:
Chatbots are the latest feature of financial services being deployed to aid and automate and reply when asked frequently asked questions, common customer service answers and requests, help in bill payments, provide information on services and products and more.

Since they work with NLP-natural language processing they understand the query and answer appropriately. But there are instances when the scenario does not fit the scripted questions and the conversation is beyond their comprehension.

ML is important to teach the chatbots in customer service to assimilate data from interactions where the AI can self-learn how to respond in the future based on the experience they gather. Obviously more the interactions, the better they get.

They are also capable of recognizing emotions like frustration, anger and so on where they can diffuse the tensions by transferring to a live customer service agent for further help or resolution. Often they up-sell products, introduce the newer services and help in transactions like making automated payments.

During the course of such interactions, they can also pick up customer behavior trends like the possibility of defaults due to cash-flows. Imagine how satisfied a customer would be when it is the due date for payment, the account is bereft of money and the chatbot work efficiently offers a different due date, a short-term loan or a customized payment plan.

That’s just a small example of the chatbot and its machine learning courses enriching the customer or user experience.

Examples in Personal Finance:
ML comes to the aid of financial institutions by specializing in the service of customers needing applications for budget management, offering guidance and highly targeted financial advice. Such apps are made for mobile devices and allow their clients to track their daily spending.

Using their innate ability to spot trends they can help with budgeting, saving and investment decisions and plans by watching and learning from the client’s spending and purchase patterns.

Ina real-life example a leading bank spotted the trend of people from a certain segment facing problems with their cash flow and using their credit cards for late-night transactions and withdrawals. By flagging such abnormal behaviour it was found that the segment faced unduly low-interest rates in their savings accounts. Based on such foresight the bank not only improved its savings rates but it also offered the segment increased credit limits to restrict defaults on payments.

ML intelligence worked very well since the bank retained its customers with such an offer and also saw an increase in its savings accounts deposits.

Examples in Fraud and Risk Management:
In the fields of risk and fraud management the daily number of transactions to be scanned, are very large and involve huge sums of money. In modern times online payments have emerged as an ideal spot for fraud perpetration. Paypal the market leaders, have employed machine learning courses specializing in risk management and fraud detection and using Big Data, complex neural networks, and deep learning capabilities. Any abnormal behavior is flagged and forms a sandboxed risk queue within milliseconds.

The cybersecurity challenges are confrontable by smart ML algorithms. The detection of phishing attacks is dependent on the algorithm being able to easily compare the original and fake sites for logos, visual images, and site components. T

hey can also detect unusual behavior once they are trained on recognizing normal patterns on a profile or account. A red flag is immediately raised and the user is asked to verify the transaction.ML is also used in risk scoring, assessing defaults in payments, automating credit scores and compliance issues, assessing loan applications and every transaction in between.

In conclusion:
Machine learning is not restricted to any one field. However, the applications can get very complex and extend far beyond these few examples. ML helps in better security, increasing operational efficiency and delivering better customer service or user experience.

If you would like to learn more, then do the machine learning courses at the Imarticus Learning Institute where technologies of tomorrow are taught and skilled for today.

Is A Machine Learning The Next Step Of Smart Learning?

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Is A Machine Learning The Next Step Of Smart Learning?

2015 was rife with stories of ML and self-driving cars. Again a decade ago people were abuzz with robots doing the repetitive tasks, and human intervention being all about seasoned judgment for complex tasks. And then, cars were self-driven and computers really proved that they were fast approaching the machine learning course of human intelligence with self-learning algorithms and AI taking over a data-driven world.

Both AI and ML started with helping the human coded programs helping computers spot data patterns and inform algorithms which made foresight, insights, decisions and data-driven predictions. And then, the volumes of data became huge and machines needed a machine learning course to be able to help humans with cleaning and sorting the data.

 

So self-learning and deep learning algorithms soon taught the AI-driven computers to get smart and self-learn. As data became larger, their ability to accurately parse and clean data and provide insights increased. And so, we developed new self-learning ML algorithms which are the latest weapons against terrorism, cybersecurity, climate change, and cancer applications.

The Master Algorithm authored by Pedro Domingos, claims machine learning is the new basis and infrastructure for all applicational algorithms. He described it as the Higher Education switchboard. When data privacy became an issue in the UK in 2014 the US K-12 dialogue led to concerns in over-testing and by the end of the year, more than a hundred vendors of EdTech voluntarily signed the pledge for data privacy.

The series on SmartParents led with the argument that personalized learning is data-based and that parents with access to such data should decide on the privacy of the data. The policymakers were forced to embrace both data privacy and personalization.

Today ML and Big Data techniques are part of our lives and play a big role in informal and formal education. Let us see how we can make learning smart with machine learning. Below we list the areas and resources available to parents and teachers alike for

1. Learning data analytics and applications in a machine learning course that can help track student knowledge while recommending further learning steps. Here are some resources for learning systems that are adaptive.

Ex: ALEKS, DreamBox, Knewton and Reasoning Mind. Here’s a treasure for game-based learning:  Mangahigh and ST Math.

2. Beginning learning on content analytics that is used to optimize and organize content-modules.

Ex: IBM Watson Content Analytics and Gooru.

3. Scheduling for math learning that teams students needing help with teacher resources in real-time and dynamically.

Ex: NewClassrooms which uses ML and data analytics to schedule mathematics learning sessions.

4. Scoring and grading systems to score and asses on a large scale the student answers to the computer and other assignments and assessments: The grading could be peer grading or automatic ML grading.

Ex:  Lightside by Turnitin and WriteToLearn by Pearson help detect plagiarism and score essays.

5. To identify new opportunities through tools for process intelligence which analyze both big unstructured and structured data while enabling the visualization of work-flow.

Ex: Clarity from BrightBytes provides a strength gap analysis by reviewing best practices and research to build evidence-based frameworks.

IBM SPSS  and Jenzabar are systems for ERP-Enterprise Resource Planning which help the formal higher education schools and institutions in enhancing campus security, improving financial aid, predicting enrollment, and boosting student retention.

6. In helping match schools and teachers like TeacherMatch and MyEdMatch which harmonize online the recruitments of teachers.

7. For learning data mining and predictive analytics from experts try Map patterns of expert teachers and Improve learning, retention, and application articles.

8. For a host of applications in the back office are school bus scheduling EDULOG, Evolution, and DietMaster.

Parting Notes:

There is no doubt that the earlier you learn the more skilled you become. To ensure smart learning in ML every student and faculty member must learn at the earliest the impact of ML, AI, and Big Data. No matter what the pathway your learning curve should be enhanced with skills at the earliest.

Do you want to join a machine learning course at the Imarticus Learning Academy where data scientists hone their skills in AI, Big data and ML? Their courses also include personality development modules and train you in the soft skills required to emerge job-ready and with the right skill sets.

Bots In Learning AI And Personalized Learning Experience

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Bots In Learning AI And Personalized Learning Experience

The class sizes keep increasing with compulsory education and teachers are often facing many challenges in giving attention and help to the large numbers of students. A big challenge like this has been simplified by incorporating computer programs that allow each student to follow his own pace and learning curve.

Since the ideal teacher-student ratio has long been overtaken, a lot of educational instructors have unobtrusively introduced AI and ML to help with self-scoring assignments, computer-aided assignments and course review modules and videos that help the learning process which tends to be different in style, pace, and manner of learning in each individual student.

However such early initiation has led to students thinking of the quickest and easiest way to beat the system. This was supposed to be a part of the personalized learning process which probably needs a review given that AI and a machine learning course have a huge role to play in the future of technologies.

Learning Bots:

The newer methods of experiential learning at educational institutions use advanced techniques of AI, machine learning and deep learning in instructing and teaching like Chatbots and learning bots.

A few examples of such learning bots are:

  • Botsify is a suite of bots that have bot assistants like the tutoring bots, FAQ bots and more.
  • Mika is a math bot tutor based on AI used widely in schools and higher education institutions.
  • Snatchbot helps administrators and teachers with templates to help customize a bot to the classroom needs and subjects.
  • Ozobot is a specialized coding bot.

AI has thus personalized the teaching and learning experience by incorporating a machine learning course for bots to enable their functioning in the field of education and instruction.

Learning supports with AI:

Individualized learning modules can help find knowledge gaps and personalize the learning materials to fill in the gaps. By so adjusting the learning rate no student in a class is way ahead or too far back on the learning curve. Since learning styles, rates and methods may vary over each student, adaptive learning scores by understanding and identifying the gap in learning and taking corrective action before it is too late.

A differentiated AI style of learning deals with the most effective style to help the student learn. Adaptive AI-based learning curates the learning exercises matching them to the student’s needs and knowledge gaps. Competency-based AI and machine learning course tests aid the students to gauge their learning levels and progress from thereon. Using all these three types of learning AI can test how well the students can adapt their learning to applications of it and thus promote the progress of students based on individual interests.

Tutoring help:

The bots have become extremely popular and the future will probably have specialized tutoring bots where the learners can ask questions and receive answers in real-time. Chatbots, tutoring bots and even bots for teachers to help score examinations, assess large volumes of answer sheets and more are being used to improve the learning and educational process. Tweaking the earlier bots have led to specialized bots that even suggest and provide resources specific to a learning style.

Administrative tasks aids:

Teaching is a challenge and scoring and grading are tasks that are repetitive and time-consuming. Multiple choice questions and online testing are AI forms of grading already in use where learning responses need not be essentially written responses. Thus a lot of paperwork and unnecessary wastage of time is eliminated.

Since bots are able to quickly analyze the responses, feedback can be near-instantaneous. Teachers can now get truly involved in teaching and rectifying the lacunae in the learning process. Besides, the teachers can also get recommendations on how to rectify the issues, what learning materials to use for personalizing the process and much more to help herd the students towards the right levels of comprehension and skills required. This could also be used for learning processes of differently challenged students.

Concluding notes:

Both bot technology and its AI technology has started the process of personalizing and improving the education system of learning. Today bots are not new to students who can exploit their benefits at will and at their own pace to learn advanced subjects. Such advancements in AI, ML and bot technologies spur demand for professionals in this emerging field which has immense potential. Would you like to do a machine learning course at Imarticus Learning and join the ranks of the highly paid professionals who face no dearth of jobs? Start today. Hurry!

For more details, you can also contact to our Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, Gurgaon, and Ahmedabad.

AI and Food: Safer and More Tasty Food?

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Reading Time: 4 minutes

 

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?

Artificial Intelligence as an Anti-Corruption Tool

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Artificial Intelligence as an Anti-Corruption Tool

It is obvious why anyone would want to put a cork on corruption and then throw it into space to disintegrate itself. So, when a group of scientists from the University of Valladolid in Spain put together a computer model that can predict instances of graft in government agencies, the whole world took notice.

Here are some of the top takeaways from the study that was published in FECYT – Spanish Foundation for Science and Technology. It was first published online in Springer on 22 November 2017.

What was the study about?

According to the research paper published in Springer, the study created a computer model based on neural networks that would send out warnings for possible instances of graft occurring in a government office. These warnings can then be used for corrective and preventive measures, which in other words, means changing the way a government functions or weeding out certain bad apples, for lack of a better term.

The model in the study uses corruption data extracted from several provinces in Spain where graft occurred between 2000 and 2012. A lot of different factors were at play that defined how a routine incident of corruption would occur in any given government agency.

The researchers’ aim was to understand what factors play a key role and then administering changes to those factors in an attempt to eradicate corruption. Of course, this process will be iterative, as no “bad habit” can be weeded out in a single go.

What were the findings?

According to the paper published by the researchers, the following are the key takeaways. Since Spain follows a customized form of parliamentary monarchy, it can be easily translated and adapted for other similar governments. Of course, the data will need to be updated.

  • Public corruption is a cause of multiple factors such as:
    • Taxation of real estate and a steady increase in property prices
    • Nature of economic growth, GDP rate, and inflation
    • Increase in the total number of non-financial institutions
    • Sustenance of any single political party for a “long time”
  • Since data on actual cases of corruption was used to create the model, it provides for a better look at the factors compared to how the model would have been had it depended solely on the perception of corruption. Such studies have not yielded much either have they garnered any interest from the public
  • Corruption can be predicted three years before they are bound to happen

Where does AI come in the picture?

Since all of this sounds too good to be true, it is wise to ask what the role of artificial intelligence is in this study. In order to do that, let us go back to other studies that have tried to predict corruption.

All of the previous studies on the subject have depended on data that were more or less subjective indexes of perception of corruption, reports Science Daily. What this means is that the only type of data being used is something that is available on the public domain. Since the government can sometimes come in between the sourcing of this data by private agencies like Transparency International, the database stops becoming useful. All it will give the model is data that does not reflect the true gravity of the situation.

On the other hand, when actual data is used, there is much for artificial intelligence to feed itself and then bring out a model that can be used to predict the very nature of corruption. This is where this new study excels when compared with historical reports.

The biggest role of artificial intelligence in this exercise is to find a correlation in a set of data through a process that attempts to mimic the human brain functionality. If we feed human being scores of detailed court cases about corruption charges on government employees, he will take decades to dig through them and still end up without an actionable conclusion.

A neural network, on the other hand, analyses the data, studies its various factors, and creates a relationship between them to see what connects with what. Let’s take a rough example to make this clear.

If out of 10 cases of corruption, 8 of them involves a particular modus operandi and a similar cause for the exchange of money to take place, then such a connectionist system will flag that as a recurring factor. This information, along with several others, is then used to reach a conclusion. Of course, this example is an imaginary one, and the amount of data fed in the study were a lot higher, which makes the result even more constructive. Over 12 years of data of actual corruption cases are bound to give such an AI tool enough ground datum to work with. But, it should still be noted that no amount of data is sufficient when one is trying to execute the predictive analysis.

Finally, according to the Chr. Michelsen Institute, this type of predictive tool that depends on patterns can well be a smart anti-corruption tool. Its ability to handle big data and detect anomalies in it is what makes it a promising new system that could be adopted by late the 2020s. However, it also points out the single biggest concern over its use: it will force for more surveillance in the world as data about even the smallest corruption cases will be added into the system. Data that will include personal details of individuals.

Conclusion

It is a great relief that AI in theory at least does not mimic the dangers portrayed by science-fiction films and is being seen as a technology that can help humanity lead a better life. Its usage in anti-corruption neutral networks is a step in the right direction, and with added research, it will pave way for better governance. Where those being governed have one less accusation to make against the government.

How Artificial Intelligence Has Changed The Way We Secure The Data?

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How Artificial Intelligence Has Changed The Way We Secure The Data?

Though the concepts have been around for ages the past two decades have seen a phenomenal 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.

The availability of very large databases of Big Data itself and the proliferation of cloud technology and cloud computing have directly contributed hugely to allowing ML/AI to sift through these very large volumes of very big data and mimic the human brain’s logic in inferential and logical predictions, gaining foresight or producing predictive insights into such data.

The figures and data volumes are mind-boggling and cannot be humanly attainable without ML/AI applications. It is estimated that by 2030 almost all businesses will use ML/AI techniques and the market value of training data sets using a Machine Learning course will see a market of 13trn USD!

AI in cybersecurity:

Cybersecurity is the most demanding and promising area for ML/AI. In theory, if the machine is given complete data both good and bad then it should throw up any pattern that is related to unusual behaviour or malware in the database. This implies that

  • Your model needs to effectively harness a huge volume of available data including malware, and good and benign data.
  • The data pipeline needs the data scientists and engineers to build and maintain a continuous process for the sampling of data sets and training effective data-based models.
  • The goal of providing insights needs to be categorized by specialists in the domain to sift the bad from the good and the process and results need to be justifiable, logical and explained.

Sad but true, is the fact that many  ML/AI security solutions lack in meeting these criteria. 

The process used:

A basic tenet of cybersecurity is a multi-security-layered defence in depth rather than just the use of ML/AI technologies while scanning the system periodically for user-accessed content. The area of file downloading should stop SSL-encrypted communications between the user-client and destination servers and allow the scanning of every file involved in order to ensure scanner perceptibility. This is time-consuming and affects UX. However, such scanning is a compromise of providing a secure user experience with the white-listed files while effectively blocking threats and malware.

Once threat intelligence has been deployed there is still the zero-day or unknown threats which loom large. Such threats are sandboxed in a virtual environment mimicking the user environment and studied before labeling them as bad or good. ML/AI techniques with the deployment of artificial intelligence course trained algos can effectively do this process almost instantly and avoid the user having to wait for long periods of time.

Hackers use exploitative kits which borrow delivery techniques and exfiltration of previous known threats and attacks which are easily identified by ML/AI models trained to identify variants that are polymorphic. Importantly, answering queries on why particular data sets are classified ‘bad’ should use the expertise of domain specialists who are capable of explaining the triggers and test results in order to ensure better and more accurate predictive models.

Training the models:

There are two kinds of learning and making prediction models. One is unsupervised learning, which is based on data structure and free from any human bias in the selection of data sets or malware features. Supervised learning, on the other hand, uses human intervention in sampling and labelling the database while using labelled data for the extracted prediction model. Which method is better depends on the suitable parameters prior to training the artificial intelligence course of algorithms that result in the predictions?

The best security areas where AI/ML can help:

The cybersecurity challenges are confrontable by smart ML/AI algorithms. The detection of phishing attacks is dependent on the algorithm being able to easily compare the original and fake sites for logos, visual images, and site components. They can also detect unusual behaviour once they are trained in recognizing normal patterns on your profile or account. A red flag is immediately raised and you are asked to verify the transaction. This makes the hacker’s job harder and your account safer and more secure.

In conclusion:

An artificial intelligence course that can train the AI/ML model under expert guidance from cybersecurity and data science experts is a valuable tool in mitigating the effects of cyberfrauds. Do your course on AI and ML at the Imarticus Learning Academy to emerge career –ready in these fields.

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, Bangalore, Hyderabad, Delhi and Gurgaon.

How Artificial Intelligence Help To Transform Employee Productivity?

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Reading Time: 3 minutes

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 To Pay Taxes Without Leaking Your Personal Data Using Machine Learning?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How To Pay Taxes Without Leaking Your Personal Data Using Machine Learning?

The cutoff date to file taxes in the US (April 15th) teaches all taxpayers many a lesson. Did you know that 1 in 3 Americans wait until April each year and get stressed when filing taxes? The major fallout of this behavior is that besides the stress and worry, one is tempted to cut corners and hurry through tax filing thereby exposing themselves to potential personal data loss or stealing.
Think about this fact. Tax scams saw a 60 percent increase in 2018 in comparison to the previous three years where the rates had actually shown a decline. That is why we are going to explore what we need to do when we file taxes to ensure safety, privacy, and efficiency of the machine learning course working behind the filing of our taxes.

Password strength:

Strong passwords can protect your online tax account. Use the IRS recommendation of a long password with at least one uppercase letter, one symbol, one lowercase letter, and one number. Avoid using your name, child’s name, pets name, DOB, school name and such easily discoverable data. Once they guess your password your account can be comprised at their will and social media sites like Facebook, Instagram, Twitter and such are always used by these scam generators to study your profile.

Watch for phishing:

Online phishing by overseas cyber criminals use websites which look very like the original IRS site to scam you. Once you enter your details on the link sent to you your data has been compromised and security breached. Here’s what to look for. An email that claims you will face legal action and prompts you to use the link to provide your personal details. Don’t panic.
Also look for giveaways like letters starting without your name or the ‘Dear Customer’ from the so-called tax department officials, poor sentence construction, grammatical errors, or threats of penal action. Periodically check your account for activity and never click on the links!

Do not use your phone for transfers:

If ever you receive phone calls from the IRS claiming you are a defaulter or have to pay a court-fine and need you to pay through phone transfers then call the IRS directly at 800-829-1040 and speak to a legitimate IRS staff member. Go ahead and block such calls as the banks, IRS or any other financial institutions have long since stopped using phone calls to call you. Even when you panic, remember smart frauds can phish over the phone with misusing the Machine Learning Course and reflecting the 911 number on your display.

VPN authentication:

Always use a VPN. The service uses remote server encryption and can go a long way when you use WiFi networks both untrusted and public networks to send your tax returns to the IRS. Hackers will need to work very hard to get past the encryption and this ensures the security of your personal data.

Stay clear of great offers:

In the USA 2 in 3 taxpayers find tax-filing complicated and use a tax returns professional to file their returns. Especially so, when in a hurry! The major issue here is to distinguish between the ethical and legal professional and the scam-master posing as one just to steal and hijack your personal data. That’s why it’s always better to plan in advance and know your tax- professional’s credentials before you employ them.
The American saying that there is no free lunch anywhere is true here too. Beware of tax professionals offering to get you huge refunds especially when you know that your financial position is not so good. Does that offer sound like a bonus from heaven? Then stop! It is a scam. If your tax professional is refusing to issue a receipt for payments, then beware. No references, only cash payments or lacking testimonials are also good cause for concern.
To watch your step, ask the tax professional for his Preparer Tax Identification Number or PTIN which needs to be mandatorily mentioned with their signature when they file returns on behalf of you. Stay abreast of the latest scams and fraud methods where the machine learning course behind your personal data could be exploited for unlawful gain.
Conclusion:
Tax season or otherwise the above-mentioned measures are effective and not time-consuming to enforce all year round while preventing data loss and personal identity thefts. To learn more about cybersecurity and the machine learning course behind the tax scams do a course at the reputed Imarticus Learning Academy. All the best with your tax returns!
For more details regarding this 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, Gurgaon, and Ahmedabad.

How AI in The Energy Sector Can Help to Solve The Climate Crisis?

Posted on by Imarticus Learning
Reading Time: 3 minutes

How AI in the Energy Sector Can Help Solve the Climate Crisis

Have you not complained about the crisis that is looming large in our environment? The news reports of untimely floods, missing rain patterns, fires in forests, carbon emissions and smog affect each and every one of us. The Davos meeting of the World Economic Forum threw up some important measures that we need to take in enabling AI, ML and technology as a whole in symbiotically tackle the climate crisis of all times.

The main cause of the changes in climate is being attributed to emissions of carbon and greenhouse gases. And each and every person in tandem with AI, technology and the big industrial players have a bounden duty to support such measures and immediately move to reduce these emissions if we wish to halt such catastrophic climate changes. Noteworthy is the funding of nearly billion dollars in such ventures by Bill Gates and Facebook’s Mark Zuckerberg.

Here is the list of the top suggestions. In all these measures one looks to technology and artificial intelligence to aid and achieve what we singularly cannot do. This is because the noteworthy improvements brought about by AI are

AI helps compile and process data:

We just are not doing enough to save our planet. The agreement between countries in Paris to be implementable means elimination of all energy sources of fossil-fuel. AI enabled with intelligent ML algorithms can go a long way in processing unthinkable volumes of data and providing us with the insight and forecasts to reverse the climatic changes, use of fossil fuels, reduction of carbon emissions, waste etc, and setting up environment-friendly green systems of operations.

AI can help reduce consumption of energy by ‘server farms’

The widespread use of digitalization has led to server farms meant to store data. According to the Project Manager, Ms. Sims Witherspoon at Deepmind the AI British subsidiary of Alphabet when speaking to DW said that they have developed a bot named Go-playing with algorithms that are “general purpose” in a bid to reduce the cooling energy of data centers of Google by a whopping 40%. This does amount to a path-breaking achievement when you consider that a total of 3 percent of the energy globally used is just used by the ‘server farms’ to maintain data!

Encouraging the big players to be guardians of the climate.
The industrial giants are using technology, AI and ML to reduce their footprints of carbon emissions. AI tools from Microsoft and Google are aiding maximized recovery of natural resources like oil, coal, etc. Though with no particular plans or place in the overall plan-of-action such measures do go a long way in preserving the environment through reduced emissions and set the trend into motion.

Using smartphone assistants to nudge for low-carbon climate-friendly changes.
The rampant use of smartphones and devices of AI makes this option possible and along with zero-click AI enabled purchases the virtual assistant bolstered through ML algorithms and tweaked infrastructure can be used to influence choices of low-carbon climatic and emission-reduction changes.

Social media can transform education and societal choices.
The biggest influencer of social change is the social media platforms like Instagram, Facebook, Twitter, etc these can be harnessed to publicize, educate and act on choices that help reduce such carbon emissions and use of resources.

The reuse mantra and future design.
Almost all designing is achieved through AI which can help us design right, have default zero-carbon designs, commit to the recycling of aluminium and steel, reward lower carbon footprints, grow and consume optimum foods and groceries and create green and clean smart cities.

Summing up the suggestions to be placed at the UN Global Summit for Good AI at Geneva, it is high time we realize that the future lies in data and its proper use through AI and empowering ML. We need new standards for use of the media and advertising digitally. All countries need to globally work to reduce the use of fossil fuels in automobiles and transportation. We must cut our emissions by half in less than a decade and this is possible through proper use of data, AI, ML, and digitization.

If you care enough to be a part of this pressing solution to environmental change, learn at Imarticus Learning, how AI has the potential to harness data and control the damage to our environment. Act today.