Integration of AI technology in classroom : a blessing in disguise?

Imagine a futuristic classroom where you as a student do not have to struggle at paying attention every second. You get access to customized learning experience because the technology identifies your individual needs and caters accordingly. Your lack of comprehension on a topic is identified in real time and your lecture is shifted to tailor your learning curve.

On the flip side, imagine being in a classroom or a lecture hall where your every move is being monitored. From the moment you step into the premises; sensors and cameras are following your every action and recording it for_ “data references”_. Your privacy is out of question and your real-time sensitive data is compromised at the expense of improved quality of teaching service.

Eventually which side do you incline towards?

Both of these scenarios are like the dual sides of a coin: integrated and inter-twirled. Artificial Intelligence aka AI technology has been the focus for improving and upgrading the present education system. Its primary focus is to improve the current learning experience for majority of its students. The scope and capabilities of the technology is infinite and the momentum is growing steadily. There has been studies conducted by various organizations to understand the impact of AI technology to evaluate their capabilities, risk and also to answer certain ethical questions. The current technology of AI in classroom premises is primarily data sensitive, pattern recognition and processing through software algorithm. These data can be easily incorporated into machine learning to built an even more powerful platform.

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How far are we with AI technology in classroom?

Most of us fail to realize that we have already integrated subtle traces of AI technology in our present education system. For instance, grammar checking or plagiarism software that are preinstalled in blackboard (online submission _platform)_are perfect examples of artificial intelligence. These software can easily detect if an assignment is a direct quote from someone else’s work without their appropriate reference or acknowledgement. Soon these kind of similar software can be upgraded to include features to detect paraphrased assignments between peer submission. They might even use machine learning (ML) to identify common practices between peers.

Other AI based education model projects that have been used in many nations include: AI based teaching model that incorporates monitoring and analyzing performance of educators and students in real-time while in their classroom. However, adaptation of this model in the western world can create controversies and concerns over privacy issues. Moreover, most students might not be comfortable learning in a classroom environment and prefer to learn at their own pace. Pupil can also have different learning styles and might not adapt well to the current teaching methodology offered in that particular lecture as its happening in real time. Hence quantitative measure of classroom performance on monitoring and analyzing real time performance might not be a suitable approach.

Certain K-12 classrooms are already incorporating AI based projects in their classroom. It allows scope for these participating students to develop critical thinking ability . It is this critical thinking ability that will eventually develop into a high tech programming skill. Both students and educators have rated such approaches as positive and allows scope for more cooperative interaction in the classroom environment.

#education #machine-learning #artificial-intelligence #ethics #software-development #deep learning

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Integration of AI technology in classroom : a blessing in disguise?
Otho  Hagenes

Otho Hagenes


Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Archie  Powell

Archie Powell


10+ Questions you Should ask Yourself Before Developing an AI Solution

So, you’d like to spice things up in your current business strategy and add an extra layer of high technology with a sophisticated AI solution. Let me just ask you one simple question first:

Are you sure it is the best move?

If your eyes sparkle with excitement as you think “Yes!”, you can skip this article and go find your perfect AI vendor.

If, however, you have even a shade of doubt — stick around. I promise that you won’t regret it!

What I have for you is 10 questions answered by Miquido’s Head of Innovation that should help anyone understand whether adopting AI technology is the right move or financial suicide for your business. Ok, no more waiting — here are the questions:

  1. Can you state your problem clearly?
  2. Can you solve your challenge without AI?
  3. What do you understand by “doing your task right”?
  4. Do you accept that your solution will never be perfect?
  5. Do you possess enough data to get started?
  6. Is your** data relevant**?
  7. Will you have continuous access to new data?
  8. Can you label your data correctly?
  9. Do you need an interpretable model or is accuracy enough?
  10. Do you have enough resources to keep the project going?

Sound simple enough? Well, it gets a bit more complicated. So we’ll look into each of them in more detail in just a second.

Get comfy, grab something to take notes with, and go along with this article, or simply save it for later.

Let’s dive in!

1. Can you state the problem clearly?

A clear vision of what you want to achieve is a must when it comes to applying AI in business. You need to have a specific problem that requires high-tech solutions that can be solved with Artificial Intelligence and/or Machine Learning.

Introducing AI for the sake of bragging about having it won’t fly here.

To help you answer this question, try thinking about specific challenges you want to address and always have your target audience in mind. Ask yourself:

  • Which repetitive tasks are you trying to automate?
  • Who will benefit from this automation?
  • Will it be worth the investment?

Pro tip

_If you’re not sure where to start — start with data mining. _Its ultimate goal is to help you get inspiration and once your idea is stated, you can come back to Machine Learning. Focus on what you can have right there and right now — the cornerstone of Artificial Intelligence, data.

2. Can you solve your challenge without AI?

Artificial Intelligence is great because it offers seemingly simple solutions to complex problems. The only issue is that, in fact, it’s far more demanding than it appears. So the ugly truth about the development of AI technology is that if you can get on without it — just do it.

Consider these questions:

  • Is your problem complex enough to engage machine learning?
  • Could it be represented by a mathematical equation?
  • Can you think of a step-by-step recipe for an output?

If you answered “yes” to the last two questions — congratulations, you **don’t **need artificial intelligence!

If, however, you couldn’t quite wrap your mind around the correct patterns, advanced machine learning algorithms might be your only chance to succeed.

Pro tip

It’s quite simple, really: if you can recognise the pattern yourself, you don’t need Machine Learning to do that for you.

By now, you should have a pretty good understanding of whether you actually need AI in your strategy or if you simply want to follow the trends. Let’s see if Artificial Intelligence actually can help you out.

3. What does it mean to do your task right?

First of all, you’ll have to define what “right” and “wrong” mean to you and your business. The world of technology is still pretty binary, and if something isn’t “true”, it is false by definition.

So, before investing in any solution, make sure you understand what it is you hope to gain from it.

Artificial intelligence is, first and foremost, a complicated algorithm. And in order for it to learn, you need to be able to evaluate its performance. Think along the lines of:

  • How will you evaluate that a task is done correctly?
  • Which mistakes will be more harmful than others?
  • How many mistakes per 1000 results can you afford to have?

That’s another big revelation about working with AI: mistakes are unavoidable.

Pro tip

Be careful not to aim too high with accuracy. Placing an excessively high bar may result in you missing some profitable opportunities.

That brings us straight to question #4:

4. Do you accept that your solution will never be perfect?

If your reply to this question is “no”, I’ve got some bad news for you: you’re not ready to work with machine learning. **There will be mistakes. **Sometimes more, sometimes less, but there’s no chance in the world for your solution to run error-free. Even if you have Elon Musk on your team.

At this point, think about yourself, your mental health, and the daily struggle you’ll have to accept from now on in your business:

  • Can you live with occasional mistakes in your model?
  • What are the actual consequences of such mistakes?
  • What does it all mean from an ethical standpoint?

Once again, there are two potential outcomes here:

  • You’ve evaluated the risks correctly, talked yourself into accepting them, and are on your merry way to hiring an AI software development company.
  • You’ve reached the conclusion that the stakes are too high, so you can either give up on the idea of working with machine learning altogether.

Pro tip

To minimise the chance of costly and dangerous mistakes, simply have someone who will double-check the results. This is known as a “human-in-the-loop approach” and can save you some headaches.

With that in mind, you must have a pretty good idea of whether or not the AI onion is worth peeling. Now it’s time to see if you’ll be able to actually build your model.

5. Do you have enough data?

The most common and broadly discussed limitation of Artificial Intelligence is its heavy dependency on datasets. There is simply no machine learning without data.

To put it bluntly: if you don’t have the data to keep your project running, your chances of launching it in the first place are slim. Some questions that might help you think are:

Do potentially useful inputs even exist? Can you gain access to them (e.g. build them, buy them, etc.)?Do you have enough examples?

Pro tip

It usually takes at least 10 thousand samples when training the model from scratch. Yet, the more examples you have the more reliable your AI model will be, and don’t we all thrive for perfection?

In data science, however, it’s both quality _and _quantity that matter, so if you do have enough materials to proceed with, let’s check if they hold any value.

6. Is your data relevant?

As you could’ve guessed from the previous point, without having a well-thought-through plan for data collection, you’ll stumble across multiple serious issues with your AI solution pretty quickly.

The best way to ensure it doesn’t happen is to double-check the actual relevance of your data.

There’s no point in having 1000+ features with no practical use, they’ll only eat up your precious storage space. The important questions to keep in mind to ensure that you’ll only work with the highest quality data points are:

  • Are your data points significant?
  • Is all the data clean enough?
  • Is the data you have relevant to your target audience?
  • Is it free of bias?

Pro tip

To learn from examples, AI needs good examples to learn from. In order for everything to work in an orderly fashion, you need to ensure your sample data is well-balanced, clean and free of inconsistencies.

And if you’ve got that covered, it’s time to think about the further evolution of your AI solution.

7. Will you be able to access new data continuously?

We are almost done with data questions, I promise! But while we’re on the subject, let’s think about the scalability of your project.

Having an accurate initial model might suffice for obtaining reliable predictions for a relatively short period of time. Yet, you’ll soon notice there are numerous factors that may negatively affect its performance. These threats include social events, seasonal changes, shifts in demographics, the geographical location of your users, etc.

When there are factors that can influence your target audience over time, your AI model needs to be constantly retrained.

Here, you will have to face new challenges and try to answer questions like:

  • How sensitive is your dataset to changes?
  • Do or will you have access to new data continuously in order to update it?

A great example of such unexpected threats is Covid-19. It has pretty much rendered a massive amount of data obsolete, as people’s behaviour has changed drastically.

Pro tip

Make sure that your AI solution will be weather-proof._ Or, in case of a disastrous setback, like a worldwide pandemic, at least can be easily updated._

That brings us to the final question about data.

8. Can you label your data properly?

We’ve covered some paramount issues like obtaining, updating, and navigating Big Data, so now it’s time to talk about data management.

In order for your AI solution to correctly understand the data, it requires proper labelling. Some datasets, such as image recognition systems, inherently contain labels based on the logged user actions. However, if your plan is to build a classification from the ground up, you’ll need to come up with a system for correct data labelling.

The important questions to think about here are:

  • Do you need to label your dataset?
  • Can you get a human to do it?
  • How much time and money will that process require?

Pro tip

_When working with advanced data, such as ECG signals or medical images, __hire experts to correctly classify each case _before your model could learn from examples.

With that, we’re ready to move on to the next question!

9. Does your model have to be interpretable?

Interpretability is one of the primary issues with machine learning. But what does it even mean?

**In the simplest terms, the higher the **interpretability of an AI training model, the easier it makes it for a human to understand the processes behind the algorithm’s decision making.

It is crucial for some businesses to fully understand the flow due to external policies and regulations. However, more often than not, having a model that is accurate yet not entirely interpretable, is enough to get your AI project up and running.

So, the two major questions that arise here are:

  • Are there any regulations governing your model’s interpretability?
  • What is your trade-off between accuracy and interpretability?

Pro tip

_Testing helps to launch many projects regardless of their complexity. _If you can’t explain how something works, run as many tests as needed to prove that it does work.

Looks like it’s time for the final and the most important question you should ask yourself before committing to an AI project:

10. Do you have enough resources to implement & maintain your AI solution?

As you may already know, machine learning projects aren’t particularly cheap, are not that easy to implement, and require an experienced team behind the wheel. So before you jump into your next idea headfirst, try to evaluate with great care whether you have enough resources to really pull this off.

Some of the questions that might help you at this stage are:

  • How much does building and updating the database cost?
  • Do you have access to enough processing power?
  • Are you aware of the costs of training and retraining your model?
  • Are the expected benefits higher than the estimated costs?

Depending on the complexity of your model, all these numbers can vary drastically.

#ai #future-of-ai #ai-technology #investing #technology #tech

Shradha Singh


AI Engineers and the Top 5 Hottest Technology Jobs |

With the world business landscape changing at an unprecedented rate and moving towards digital business operations, the relevance of technology skillset has grown manifolds off late. Some of the most sought after skills in the global job markets today is technical skills, especially the skillset in disruptive techs such as AI, machine learning, data science, and automation. Recruiters across industry domains are desperately seeking candidates with relevant skills in advanced technologies. If one is skilled in emerging tech and also officially certified in the same, he is all set to become a unicorn of the technology sector.


AI architects are supposed to find and develop AI solutions for the organization’s specific business needs. They are required to possess knowledge in AI application programming, AI integration, and NLP (natural language processing). Soft skills do play a vital role in being an AI engineer. You need to be a strategic thinker with robust communication skills.

The adoption rate of AI tech across industry domains is what is making it so special for businesses worldwide, and hence the growing need for AI architects. For AI aspirants, the best aid could be enrolling in some of the best AI certification programs available online.

These technology professionals develop infrastructure, safeguard the firm’s cloud against hacking, malware attacks, and viruses. The other responsibilities of DevOps professionals comprise keeping in check the health of the technology tools used in business operations, ensuring and developing process automation, detecting & solving queries related to critical areas of software development such as testing.

#ai architects #ai tech #technology jobs #ai architects #ai solutions #devops engineers

Murray  Beatty

Murray Beatty


This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

Meggie  Flatley

Meggie Flatley


Chatbot Integrations – Adding an Integration in Teneo - DZone AI

If you want your chatbot to be able to know the answer to more than just the things you teach it about your business you can integrate to other services. Why invent the wheel twice? With integrations, you can have loads of information that might change from day to day without having to constantly update your solution manually.

Let’s look into how you add an integration in Teneo Studio.

To correctly handle an input of a user, a bot may need to connect to external services. For example, you may want to provide weather information or your bot may need to initiate a reset password process. In Teneo these calls to external services and the handling of their responses are carried out by Integrations. You add an integration to your solution once, after that it’s available for any flow in that solution.

In this example, we will create a flow that uses an integration that provides the number of calories in a coffee, like this:

User: How many calories in a flat white?

Bot: One flat white contains about 223 calories, a walk of about 56 minutes should be enough to burn them.

To make this possible, we are first going to set up an integration and then create a flow that makes use of the integration. The final result will look like this:

Set up the Nutrition Integration

First, we’re going to set up the integration. These are the steps to add an integration in Teneo:

  1. Open the ‘Solution’ tab in the solution’s main window and select ‘Resources’ in the purple bar on the left hand side
  2. Select ‘Integration’ at the top
  3. Click the ‘Add’ button to create an integration
  4. Name the integration Nutrition
  5. Click the ‘back button’ in the top left to leave the integration’s backstage view so that you enter the main integration view

Set up a Method

An integration can contain multiple methods. A method is a block of code which only runs when it is called. You can pass data into the method and after executing the code, the method return the results. Here we want to create a method that returns the calories and walking duration to burn these calories for the coffee drink that was passed into the method.

When you created the integration, a ‘Default Method’ was automatically created. Let’s give it a proper name and add an input parameter we will use to pass data into the method and add two output parameters that we will use to return the results:

  1. Re-name the method by replacing ‘Default Method’ in the ‘Name’ field with Get calories and add the description Returns calories for a given drink and the walking duration required 
  2. to burn the calories.
  3. On the right, click on the ‘Inputs’ and the ’Outputs’ tab to see the input and output parameters (if not yet visible already).
  4. Click ‘Add’ in the input parameters panel to add a new input parameter (for the coffee to find the calories for) and give it a name and a description:
  • Name the input parameter query.
  • Add the description: The coffee drink to find the calories for. For example: 'cappuccino' 
  • or 'espresso'.
  1. Now let’s add an output parameter for the calories found. Click ‘Add’ in the output parameters panel and name it as follows:
  • Name: calories.
  • Description: The calories found.
  1. Finally, add the last output parameter for the walking duration:
  • Name: walkingDuration.
  • Description Walk duration in minutes to burn the calories.

Add the Script to the Method

To complete the method, let’s add the script which will be executed when the integration is called by the flow and save the method:

#integration #machine learning #chatbot #rpa #chatbot development #conversational ai #ai artificial intelligence #ai chatbots #cpaas