365 Data Science

Applications of Artificial Intelligence in Social Media

Social networks constantly gather unlimited amounts of data. It is practically unthinkable for humans to sort this data, and examine it, or even utilise it. However, we do have artificial intelligence applications that are embedded in these social media frameworks. As a consequence, this technology takes different forms.

Chatbots

Chatbots are an artificial intelligence software that can maintain a conversation or a discussion with a user using natural language on different platforms such as email applications, websites or mobile applications (Dagnon, 2018; Frankenfield, 2018). Chatbots are able to respond quickly, in very superior and completely assuring expressions in the interaction between computer systems and humans. However, on the technical side, chatbots are only a fundamental evolution of a question-and-answer system based on natural language processing (Frankenfield, 2018).

Applications that use chatbot technology humanise dialogues between machines and people, thereby improving the customer experience. Moreover, they contribute to companies’ great opportunities to develop the customer integration process while optimising customer services.

In order to produce good results, the chatbot has to accomplice two responsibilities. We have to keep in mind that human intervention is required to develop, monitoring and optimising the chatbot’s technology framework. (Dagnon, 2018).

Chatbots have several functions. They can guide potential customers to brands and products in instant messaging applications, think of it as a chatbox when you visit an e-commerce website. The chatbot’s function will help you in navigating the website whilst creating a better user experience for the user with the brand itself. Businesses can use chatbots to have automated interactive conversations with website’s visitors and help them answer their queries 24/7. Chatbots are also great for accomplishing this task; they also track and analyse customer-shopping history. With this overview of Internet users’ behaviour, the brands can at any time, modify and retarget the digital campaigns in favour of the recommendations made by the collected data, thus increasing the rate of conversion. Most customer inquiries and complaints are resolved quickly with chatbots. They can answer FAQs, track shoppers through the numerous processes and render fast and efficient customer service. The use of chatbots can handle simple inquiries, so businesses release sales and customer relationship teams to focus on demanding responsibilities.

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Applications of Artificial Intelligence in Social Media was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/applications-of-artificial-intelligence-in-social-media-7f2db8e9784d?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/applications-of-artificial-intelligence-in-social-media

How Will AI Change the Face of Digital Marketing in 2021?

Artificial Intelligence — to many it might remind of some “terminator sci-fi” where AI overruns humanity. However, in the real world, artificial intelligence has marked a huge impact in science, economics, and business. Today, brands are very dependent on these AI mechanisms to sustain the market and its trends.

In popular opinion, if there is any change that is really visible in society, it is the scientific development of civilization and human dependence on it. Well, in reference to AI applications in digital marketing, I have expressed myself below.

AI is revolutionizing digital strategies with its operative learning system. It has the ability to store data, analyze it, and learn from the analysis in order to improve. There isn’t any limit to this and due to the speed and accuracy, it is very competitive tech.

It is still in the cocoon phase and advancing every single day. It is definitely a priority for many companies who are focused on the market and want to enhance the digital marketing strategy. This technology provides sufficient customer insights which assist in deeper penetration within the industry.

Apart from that, AI systems are inducted in projects of companies, like Tesla’s auto-driver, Apple’s Siri, Google, etc. It looks like in the forthcoming years it would become the norm to include AI in most of the simple equipment.

Endless numbers of enterprises are involving AI in their business goals. Artificial Intelligence is essential for the future of digital marketing.

The relation between AI and Digital Marketing

Customer Relationship Management is a department that creates a strategy to maximize business with a customer-centric approach. When CRM is combined with Artificial Intelligence, it assists in gaining insights about the targeted customers by maximizing the collection of user data from various platforms. This then can be utilized to identify the needs of the consumers eventually to deploy the most accurate marketing strategy.

Marketers can sort out data after thorough analysis and improve AI algorithms to achieve higher sales. With such a deeply-placed data collection mechanism, companies can access the customers’ behavior and put up the most effective solution for them. This makes the user-experience hassle-free and retention of customers increases for the company.

The aim now is to understand some of the new trends of AI in Digital Marketing that may occur in 2021. Suggested changes are an effort of thorough analysis and aim at preparing every writer or company for 2021.

Automated Customer Services or Chatbots

Eminent growth in the utilization of chatbots is noticeable in recent years. They have become institutional while dealing with simple inquiries or marketing your products. AI like Alexa for Amazon has already made the search easy for the customers buying from the shopping portal. The popularity of virtual assistants is huge and feels to be comfortable working within the interface.

A Global AI Survey Report of 2016 suggests that these self-learning AI systems are supported by around 86% of people in the US, UK, Germany, and Latin America, collectively. These supporters are curious about trying fresh products based on the system.

In fact, 65% of customers are already interacting with some kind of AI tool. Various companies are trying to develop this chatbot technology and the future for such tech is bright.

Further, here are the top 5 benefits of using chatbot for your business:

24×7 availability of chatbots

2. Chatbots will save cost on resources

3. Chatbots decrease the chances of error

4. Chatbots are AI-based, unaffected by emotions, thereby guaranteed
customer satisfaction

5. Self-learning program which is faster than any employee

Increased proportion of Voiced Searches

Virtual assistants, like Alexa and Siri, are much better from the previous years. They have encapsulated the customers with their simple user interface and search accuracy. This has eventually drawn a higher number of people to use voice searches. Moreover, this inclination towards hands-free data input is expected to increase even more.

Today, almost 60% of business searches are speech searched. In 2020, more than half of the daily searches were voiced. This year will also replicate a similar trend and predictions are that 50% of every kind of search would be voice-based. This growth is not magic, but the hard work of programmers. They developed critical machine learning algorithms in order to understand human speech which resulted in these kinds of advancements in Artificial Intelligence.

If you remember how Google voice searches were 10 years back, you would know it was underdeveloped. However, Google’s algorithms are at a point where they can comprehend human speech with 96% accuracy.

Doesn’t it boggle your mind?

As human civilization becomes more comfortable with technology, it transcends to adopt an easier lifestyle. Using voice command over type search is very basic, yet changes a lot in the digital marketing industry. Businesses will need to alter their content strategies and SEO work. They will have to focus more on the voice search data in order to align better and land successful campaigns.

Increasing Demand for AR and VR

Virtual and Augmented Reality are much popular in the gaming world, like Xbox gaming and Pokémon Go. Moreover, this technology is more cheered for entertainment purposes than practical.

Further, the incorporation of AI in AR and VR will gain excessive momentum in the coming decade. It will become an important tool to enhance brand learning and marketing.

In digital marketing, this method can bring forth different ways for the customers to interact with the brand and create a connection with it. Use of AI with AR/VR will redefine user experience and build a robust brand identity.

Conclusion

AI is a valuable tool for digital marketers around the work. There is no denying in the fact that AI is influencing the choices of consumers, providing them with relevant recommendations and 24×7 swift customer services.

Finding ways to inculcate AI in a business will enable the growth of the brand, drive sale figures, and meet consumers’ expectations. So, get prepared for 2021 and sharpen your marketing tools with Artificial Intelligence.

Don’t forget to give us your ? !

How Will AI Change the Face of Digital Marketing in 2021? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/how-will-ai-change-the-face-of-digital-marketing-in-2021-4a43e8e2ddf0?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/how-will-ai-change-the-face-of-digital-marketing-in-2021

The Best Data Science Project to Have in Your Portfolio

If you are trying to find your first path into a Data Science career, then demonstrating the quality of your skills can be the greatest hurdle. While many standard projects exist for anyone to complete, creating an original data-driven project that attempts to solve some challenge is worth so much more. A good Data Scientist is one that can solve data-related questions, and a great Data Scientist poses original data-related questions and then solves.

Originally from KDnuggets https://ift.tt/3tCI4yv

source https://365datascience.weebly.com/the-best-data-science-blog-2020/the-best-data-science-project-to-have-in-your-portfolio

Essential Math for Data Science: Introduction to Matrices and the Matrix Product

As vectors, matrices are data structures allowing you to organize numbers. They are square or rectangular arrays containing values organized in two dimensions: as rows and columns. You can think of them as a spreadsheet. Learn more here.

Originally from KDnuggets https://ift.tt/2MCA4Nk

source https://365datascience.weebly.com/the-best-data-science-blog-2020/essential-math-for-data-science-introduction-to-matrices-and-the-matrix-product

Why You Should Ditch Your In-House Training Data Tools (And Avoid Building Your Own)

At Cord, we’ve spent weeks interviewing data scientists, product owners, and distributed workforce providers. Below are some of our key learnings and takeaways for successfully establishing and scaling a training data pipeline.

If you’ve ever dabbled in anything related to machine learning, chances are you’ve used labeled training data. And probably lots of it. You might even have gone through the trouble of labeling training data yourself. As you have most likely discovered, spending time creating and managing training data sucks — and it sucks even more if you can’t find an open-source tool that fits your specific use case and workflow.

Building custom tools might seem like the obvious choice, but making the first iteration is typically just the tip of the iceberg. More start- and scale-ups than we can count end up spending an insurmountable amount of time and resource building and maintaining internal tools. Making tools is rarely core to their business of building high-quality machine learning applications.

Here are things to consider when establishing your training data pipeline and when you might want to ditch your in-house tools.

Is It Built To Scale?

You’ve produced the first couple of thousand labels, trained a model, and put it into production. You begin to discover that your model does poorly in specific scenarios. It could be that your food model infers a tomato as an orange in dim lighting conditions, for example. You decide to double or even triple your workforce to keep up with your model’s insatiable appetite for data to help solve these edge cases. If your tool is built on top of CVAT — like most of the machine vision teams we’ve worked with — it quickly starts to succumb to the increased workload and comes down crashing faster than you can say Melvin Capital.

Cost Grows with Complexity

Machine learning is an arms race. Keeping up with the latest and greatest models require you to re-evaluate and update your training data. That typically means that the complexity of your label structure (ontology) and data grows, requiring you to add new features to your in-house tools continuously. New features take time to build and will be around to maintain long after, eating up precious resources from your engineering team and dragging down your expensive workforce’s productivity. This cost is not immediately apparent when you are first building out a pipeline but can become a considerable drag on your team as your application grows.

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Why You Should Ditch Your In-House Training Data Tools (And Avoid Building Your Own) was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/why-you-should-ditch-your-in-house-training-data-tools-and-avoid-building-your-own-ef78915ee84f?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/why-you-should-ditch-your-in-house-training-data-tools-and-avoid-building-your-own

Top 5 Open-Source Machine Learning Recommender System Projects With Resources

A recommender system, or a recommendation system, is a subclass of information filtering system that seeks to predict the “rating” or…

Continue reading on Becoming Human: Artificial Intelligence Magazine »

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source https://365datascience.weebly.com/the-best-data-science-blog-2020/top-5-open-source-machine-learning-recommender-system-projects-with-resources

How do machines learn: a simple explanation

How Do Machines Learn: A Simple Explanation

Get the fundamentals straight before you jump into AI

My introduction to the world of data science was through a university course called Introduction to Machine Learning I took during my bachelor’s. And it was, by far, my least favorite class of all time. Even though I was a generally high-scoring student, I struggled with it a lot.

At the time, I blamed the professor of the course for not doing a good job explaining things. Though, in retrospect, I think the main problem was my perspective.

Machine Learning was a new way of approaching problems and I didn’t take enough time to internalize the logic behind how it worked. As a result, I had a hard time learning the ML algorithms and fundamental concepts.

I recognize similar struggles in the newcomers to this field. That’s why I’d like to share a simple explanation of how machine learning works, in hopes that it will make your learning smoother than mine.

By now you have probably heard this explanation: ML algorithms learn like humans. You give it examples and it recognizes and remembers the patterns in them. You need to give it a lot of examples, though, so that it can learn accurately.

Okay, that’s clear. But HOW does it learn?

You have probably also seen this image somewhere:

This image illustrates the sentence above on a more formal level. You provide examples (input) to the model. Based on this input, the model learns and is then able to produce predictions (output).

The learning happens inside the model. And what the model is, is basically a mathematical equation. Like this one:

a*x+b = y

It says a times x plus b makes y. In the context of machine learning, the input we provide would be x and the result of the equation, y, would be the output or the prediction if you will.

The goal of machine learning is to find the a and b values that hold true for all the x and y pairs.

So basically the whole learning process is, getting examples for x and y, and based on those examples, trying to figure out what the a and b values should be by making guesses and improving those guesses.

Machine Learning models need enough data to learn accurately

What is enough data? It is the amount of data points that will help us find the most accurate explanation (line) for the problem (equation) in our hands. And yes, you guessed it, this amount depends on the complexity of the problem and the model.

Sometimes multiple explanations (or lines) might fit the data but we need enough data to figure out the most accurate explanation. In the above example, our problem could an be represented as a straight line and the model only needs 2 points to find it accurately. One point is not enough because multiple lines can pass through the same point.

Multiple lines pass through the same point.

The actual problems ML deals with is more complex. The relationship between input and output is not linear: making the line non-linear. Additionally, there would be more than one input variable, making the problem multi-dimensional and effectively impossible to plot after the 3rd dimension. This would mean we need many more data points.

Not just any data though, relevant data

We need a lot of data for an accurate model, but these data points we train our model on, need to be comprehensive. They need to represent a significant portion of the real world. Otherwise, even a (seemingly) perfect model would fail in the real world. Just like in our example how we didn’t know that the correct a, b pair was 4 and -8 before we saw the second data point, our model needs to have access to enough diversity in data points to learn an accurate enough estimation of the world.

Neglecting data quality and diversity might cause bias and unfairness in our practice too. One example is a classification algorithm having a bias in favor of men when it comes to classifying who can be the president of a country caused by the training set having 95% of the data points being male presidents and only 5% women.

Inputs might have many shapes

The input can be numerical like in our example here but it can also be categorical. Or it can be text or even an image. A good thing to know is that no matter how the input looks, before we feed it to the model, we translate it into a numerical form. For example, an image of 100×100 pixels will turn into a 100×100=10000 elements long list. Each element representing one pixel’s color with a number.

Machine Learning is an estimation

In data science, we work with problems that do not have a perfect mathematical explanations.

The example we worked with in this article can be solved with a couple of example data points because it is a mathematical formula. But there are cases where the problem is not. Especially in problems where humans are involved. One example is credit risk score calculation. If you want to calculate the likelihood someone will pay their debt back without any problems, you are dealing with the human factor. And no matter how many variables you take into account (employed/unemployed, sex, age, residence, and so on) there can always be someone who lies out of the pattern.

There is randomness to these problems. That’s why, no matter how many data points you give as examples, what we are doing at the end of the day is an estimation. We are trying to come up with a mathematical explanation that can closely estimate what happens in the real world.

The equation looks different in every algorithm

This mathematical equation that needs to be solved is different for each algorithm. For linear regression, it does actually look like the example we worked on. But for example in decision trees, the constant values we are trying to find are values at which the tree makes a decision to make a split. But let’s not get into the details here. Just know that the working logic is the same but the what the constants are is different for each ML algorithm.

And that’s all. How a machine learns on a high level is actually this simple. understanding this helped me greatly when learning about new machine learning algorithms. The trick is understanding what is being optimized and what the value I’m trying to estimate is.

Talking about all this and understanding the logic behind machine learning is one thing but practice is what will get you a job. If you’d like to learn all of this and more in practice, go to Hands-on Data Science: Complete your first portfolio project.

Don’t forget to give us your ? !

How do machines learn: a simple explanation was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Via https://becominghuman.ai/how-do-machines-learn-a-simple-explanation-2edf1fb4830e?source=rss—-5e5bef33608a—4

source https://365datascience.weebly.com/the-best-data-science-blog-2020/how-do-machines-learn-a-simple-explanation

Deep learning doesnt need to be a black box

The cultural perception of AI is often suspect because of the described challenges in knowing why a deep neural network makes its predictions. So, researchers try to crack open this “black box” after a network is trained to correlate results with inputs. But, what if the goal of explainability could be designed into the network’s architecture — before the model is trained and without reducing its predictive power? Maybe the box could stay open from the beginning.

Originally from KDnuggets https://ift.tt/3pU5HAf

source https://365datascience.weebly.com/the-best-data-science-blog-2020/deep-learning-doesnt-need-to-be-a-black-box

Backcasting: Building an Accurate Forecasting Model for Your Business

This article will shed some light on processes happening under the roof of ML-based solutions on the example of the business case where the future success directly depends on the ability to predict unknown values from the past.

Originally from KDnuggets https://ift.tt/2YKWvCv

source https://365datascience.weebly.com/the-best-data-science-blog-2020/backcasting-building-an-accurate-forecasting-model-for-your-business

365 Data Science

Applications of Artificial Intelligence in Social Media

Trending AI Articles:

Don’t forget to give us your ? !

How Will AI Change the Face of Digital Marketing in 2021?

The relation between AI and Digital Marketing

Trending AI Articles:

Automated Customer Services or Chatbots

Increased proportion of Voiced Searches

Doesn’t it boggle your mind?

Increasing Demand for AR and VR

Conclusion

Don’t forget to give us your ? !

The Best Data Science Project to Have in Your Portfolio

Top Stories Feb 1-7: How to create stunning visualizations using python from scratch; How to Get Your First Job in Data Science without Any Work Experience

Essential Math for Data Science: Introduction to Matrices and the Matrix Product

Why You Should Ditch Your In-House Training Data Tools (And Avoid Building Your Own)

Trending AI Articles:

Don’t forget to give us your ? !

Top 5 Open-Source Machine Learning Recommender System Projects With Resources

How do machines learn: a simple explanation

How Do Machines Learn: A Simple Explanation

Get the fundamentals straight before you jump into AI

Trending AI Articles:

Machine Learning models need enough data to learn accurately

Not just any data though, relevant data

Inputs might have many shapes

Machine Learning is an estimation

The equation looks different in every algorithm

Don’t forget to give us your ? !

Deep learning doesnt need to be a black box

Backcasting: Building an Accurate Forecasting Model for Your Business