The post UNCOVERED! COVID-19 & CONSUMER BEHAVIOR appeared first on Affine.

]]>There is a conscious buying behavior centered around the most basic needs with people currently buying local and embracing digital commerce.

Will COVID-19 permanently change consumer behavior?

With global pandemic changing consumer sentiment, it becomes more important than ever to have an overview of the market.

**Key Takeaways:**

- Impact of Pandemic on business & key measures to mitigate risk (CPG, Retail, SCM, Gaming)
- Digital Transformation is Imperative amidst COVID crisis
- Understanding consumer behavior and emerging trends

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]]>The post COVID-19: How can organizations survive successfully in current times? appeared first on Affine.

]]>We are all taking part in History with the Coronavirus pandemic bringing the world to a standstill. Managers and leaders across the Globe are new to handling work at home situations as organizations are forced to adapt to a new work culture.

Although a novelty for many, this arrangement is becoming increasingly popular. While the physical world is on hold, the digital world has become busier than ever.

How can companies make home offices a success? Has this become the new normal?

**For more information, join Affine’s webinar with experts discussing:**

1. The challenges people face while working from home and guidance on how to deal with it.

2. Why some companies thrive when others fail in times of crisis?

3. And what to expect when we finally resume our normal lives?

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]]>The post Labor Optimization appeared first on Affine.

]]>In this case study, we will learn on how different analytical solutions provided to cut costs and increase sales volume of stores

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]]>The post Business Impacts of COVID-19 on Production & Manufacturing appeared first on Affine.

]]>Tapas Ray (ABInBev)

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]]>The post Bayesian Theorem: Breaking it to simple using pymc3 Modelling appeared first on Affine.

]]>This article edition of Bayesian Analysis with Python introduced some basic concepts applied to the Bayesian Inference along with some practical implementations in Python using PyMC3, a state-of-the-art open-source probabilistic programming framework for exploratory analysis of the Bayesian models.

The main concepts of Bayesian statistics are covered using a practical and computational approach. The article covers the main concepts of Bayesian and Frequentist approaches, Naive Bayes algorithm and its assumptions, challenges of computational intractability in high dimensional data and approximation, sampling techniques to overcome challenges, etc. The results of Bayesian Linear Regressions are inferred and discussed for the brevity of concepts.

**Introduction**

Frequentist vs. Bayesian approaches for inferential statistics are interesting viewpoints worth exploring. Given the task at hand, it is always better to understand the applicability, advantages, and limitations of the available approaches.

In this article, we will be focusing on explaining the idea of Bayesian modeling and its difference from the frequentist counterpart. To make the discussion a little bit more intriguing and informative, these concepts are explained with a Bayesian Linear Regression (BLR) model and a Frequentist Linear Regression (LR) model.

**Bayesian and Frequentist Approaches**

**The Bayesian Approach:**

Bayesian approach is based on the idea that, given the data and a probabilistic model (which we assume can model the data well), we can find out the posterior distribution of the model’s parameters. For e.g.

In Bayesian Linear Regression approach, not only the dependent variable *y,* but also the parameters(β) are assumed to be drawn from a probability distribution, such as Gaussian distribution with mean=β^{T}X, and variance =*σ*^{2}I (refer equation 1). The outputs of BLR is a distribution, which can be used for inferring new data points.

The Frequentist Approach, on the other hand, is based on the idea that given the data, the model and the model parameters, we can use this model to infer new data. This is commonly known as the Linear Regression Approach. In LR approach, the dependent variable (y) is a linear combination of weights term-times the independent variable (x), and e is the error term due to the random noise.

Ordinary Least Square(OLS) is the method of estimating the unknown parameters of LR model. In OLS method, the parameters which minimize the sum of squared errors of training data are chosen. The output of OLS are “single point” estimates for the best model parameter.

Let’s get started with Naive Bayes Algorithm, which is the backbone of Bayesian machine learning algorithms. Here, we can predict only one value of *y*, so basically it is a point estimation

**Naive Bayes algorithm for classification **

Discussions on Bayesian Machine Learning models require a thorough understanding of probability concepts and the Bayes Theorem. So, now we discuss Bayes’ Algorithm. Bayes’ theorem finds the probability of an event occurring, given the probability of an already occurred event. Suppose we have a dataset with 7 features/attributes/independent variables (x_{1}, x_{2, }x_{3},…, x_{7}), we call this data tuple as **X**. Assume H is the hypothesis of the tuple belonging to class C. In Bayesian terminology, it is known as the *evidence*. *y* is the dependent variable/response variable (i.e., the class in classification problem). Then Mathematically, Bayes theorem is stated as :

Where:

- P(H|X) is the probability that the hypothesis H holds correct, given that we know the ‘evidence’ or attribute description of X. P(H|X) is the probability of H conditioned on X, a.k.a., Posterior Probability.
- P(X|H) is the posterior probability of X conditioned on H and is also known as ‘Likelihood’.
- P(H) is the prior probability of H. This is the fraction of occurrences for each class out of total number of samples.
- P(X) is the prior probability of evidence (data tuple X), described by measurements made on a set of attributes (x
_{1}, x_{2, }x_{3},…, x_{7}).

As we can see, the posterior probability of H conditioned on X is directly proportional to likelihood times prior probability of class and is inversely proportional to the ‘Evidence’.

**Bayesian approach for regression problem:** **Assumptions of Bayes theorem, given a sales prediction problem with 7 independent variables.**

i) Each pair of features in the dataset are independent of each other. For e.g., feature x_{1} has no effect on x_{2}, & x_{2} has no effect on feature x_{7}.

ii) Each feature makes an equal contribution towards the dependent variable.

**Finding the posterior distribution of model parameters is computationally intractable for continuous variables, we use Markov Chain Monte Carlo and Variational Inferencing methods to overcome this issue.**

From Naive Bayes theorem (equation 3), posterior calculation needs a prior, a likelihood and evidence. Prior and likelihood are calculated easily as they are defined by the assumed model. As P(X) doesn’t depend on H and given the values of features, the denominator is constant. So, P(X) is just a normalization constant. We need to maximize the value of numerator in equation 3. However, the evidence (probability of data) is calculated as:

Calculating the integral is computationally intractable with high dimensional data. In order to build faster and scalable systems, we require some sampling or approximation techniques to calculate the posterior distribution of parameters given in the observed data. In this section, two important methods for approximating intractable computations are discussed. These are sampling-based approach. Markov-chain Monte Carlo Sampling (MCMC sampling) and approximation-based approach known as Variational Inferencing (VI). Brief introduction of these techniques are as mentioned below:

**MCMC**– We use sampling techniques like MCMC to draw samples from the distribution, followed by approximating the distribution of the posterior. Refer to George’s blog [1], for more details on MCMC initialization, sampling and trace diagnostics.**VI**– Variational Inferencing method tries to find the best approximation of the distribution from a parameter family. It uses an optimization process over parameters to find the best approximation. In PyMC3, we can use Automatic Differentiation Variational Inference (ADVI), which tries to minimize the**Kullback–Leibler**(KL) divergence between a given parameter family distribution and the distribution proposed by the VI method.

**Prior Selection: Where is the prior in data, from where do I get one? **

Bayesian modelling gives alternatives to include prior information into the modelling process. If we have domain knowledge or an intelligent guess about the weight values of independent variables, we can make use of this prior information. This is unlike the frequentist approach, which assumes that the weight values of independent variables come from the data itself. According to Bayes theorem:

Now that the method for finding posterior distribution of model parameters are being discussed, the next obvious question based on equation 5 is how to find a good prior. Refer [2] for understanding how to select a good prior for the problem statement. Broadly speaking, the information contained in the prior has a direct impact on the posterior calculations. If we have a more “revealing prior” (a.k.a., a strong belief about the parameters), we need more data to “alter” this belief. The posterior is mostly driven by prior. Similarly, if we have an “vague prior” (a.k.a., no information about the distribution of parameters), the posterior is much driven by data. It means that if we have a lot of data, the likelihood will wash away the prior assumptions [3]. In BLR, the prior knowledge modelled by a probability distribution is updated with every new sample (which is modelled by some other probability distribution).

**Modelling using PyMC3 library for Bayesian Inferencing**

Following snippets of code (borrowed from [4]), shows Bayesian Linear model initialization using PyMC3 python package. PyMC3 model is initialized using “with pm.Model()” statement. The variables are assumed to follow a Gaussian distribution and Generalized Linear Models (GLMs) used for modelling. For an in-depth understanding on PyMc3 library, I recommend Davidson-Pilon’s book [5] on Bayesian methods.

**Fig. 1 Traceplot shows the posterior distribution for the model parameters as shown on the left hand side. The progression of the samples drawn in the trace for variables are shown on the right hand side. **

We can use “Traceplot” to show the posterior distribution for the model parameters and shown on the left-hand side of Fig. 1. The samples drawn in the trace for the independent variables and the intercept for 1,000 iterations are shown on the right-hand side of the Fig 1. Two colours – orange and blue, represent the two Markov chains.

After convergence, we get the coefficients of each feature, which is its effectiveness in explaining the dependent variable. The values represented in red are the Maximum a posteriori estimate (MAP), which is the mean of the variable value from the distribution. The sales can be predicted using the formula:

As it is a Bayesian approach, the model parameters are distributions. Following plots show the posterior distribution in the form of histogram. Here the variables show 94% HPD (Highest Posterior Density). HPD in Bayesian statistics is the *credible interval, *which tells us we are 94% sure that the parameter of interest falls in the given interval (for variable x_{6}, the value range is -0.023 to 0.36).

We can see that the posteriors are spread out, which is an indicative of less data points used for modelling, and the range of values each independent variable can take is not modelled within a small range (uncertainty in parameter values are very high). For e.g., for variable x_{6}, the value range is from -0.023 to 0.36, and the mean is 0.17. As we add more data, the Bayesian model can shrink this range to a smaller interval, resulting in more accurate values for weights parameters.

**When to use linear and BLR, Map, etc. Do we go Bayesian or Frequentist?**

The equation for linear regression on the same dataset is obtained as:

If we see Linear regression equation (eq. 7) and Bayesian Linear regression equation (eq. 6), there is a slight change in the weight’s values. So, which approach should we take up? Bayesian or Frequentist, given that both are yielding approximately the same results?

When we have a prior belief about the distributions of the weight variables (without seeing the data) and want this information to be included into the modelling process, followed by automatic belief adaptation as we gather more data, Bayesian is a preferable approach. If we don’t want to include any prior belief and model adaptions, the weight variables as point estimates, go for Linear regression. Why are the results of both models approximately the same?

The maximum a posteriori estimates (MAP) for each variable is the peak value of the variable in the distribution (shown in Fig.2) close to the point estimates for variables in LR model. This is the theoretical explanation for real-world problems. Try using both approaches, as the performance can vary widely based on the number of data points, and data characteristics.

**Conclusion**

This blog is an attempt to discuss the concepts of Bayesian inferencing and its implementation using PyMC3. It started off with the decade’s old Frequentist-Bayesian perspective and moved on to the backbone of Bayesian modelling, which is Bayes theorem. Once setting the foundations, the concepts of intractability to evaluate posterior distributions of continuous variables along with the solutions via sampling methods viz., MCMC and VI are discussed. A strong connection between the posterior, prior and likelihood is discussed, taking into consideration the data available in hand. Next, the Bayesian linear regression modelling using PyMc3 is discussed, along with the interpretations of results and graphs. Lastly, we discussed why and when to use Bayesian linear regression.

**Resources:**

The following are the resources to get started with Bayesian inferencing using PyMC3.

[1] https://eigenfoo.xyz/bayesian-modelling-cookbook/

[2] https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations

[3] https://stats.stackexchange.com/questions/58564/help-me-understand-

bayesian-prior-and-posterior-distributions

[4] https://towardsdatascience.com/bayesian-linear-regression-in-python-

using-machine-learning-to-predict-student-grades-part-2-b72059a8ac7e

[5] Davidson-Pilon, Cameron. *Bayesian methods for hackers: probabilistic *

*programming and Bayesian inference*. Addison-Wesley Professional, 2015.

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]]>The post Defying the COVID-19 with an Unstoppable Work-Force appeared first on Affine.

]]>**Manas Agrawal**, **Affine’s CEO**, recently shared an email to his employees, urging them to stay healthy and work from home amidst the recent developments in COVID-19 situation.

Team,

I trust that you, your families/communities are safe and doing well this season. We acknowledge the unprecedented times we are in, owing to the constantly changing COVID-19 situation, and our hearts and thoughts go out to each and every one of you.

While we are affected by the impact of COVID-19 in all aspects of our lives, I am sad to tell you that things may get worse before they get better. With no manuals to guide us through this fast-changing workflow, please remember to have deep empathy and understanding of each other’s situation.

And as we travel through unchartered territories, I understand that some of you may feel that all this is a little unsettling and overwhelming. Remind yourselves to stay grounded with a sense of purpose and the importance of acting as a community in trying times like these.

We are working with the senior leadership teams to support you in the best ways possible, prioritizing your health and safety. Our resolve is to empower individuals as we will not be able to solve a challenge like this on our own. While technology has a significant role in accelerating progress for solutions to pandemics such as this, the private and public sectors will have to work together to turn the tide on COVID-19.

I’m not alone in being grateful for the exceptional work you are all doing for Affine. The management is noticing your efforts at every level. I want to congratulate everyone for successfully achieving this Work From Home scenario without any disruptions in your deliverables. Your diligence, self-motivation, and dedication in going the extra mile are admirable.

Remember to focus on what you can do to make the world a better place. Our collective efforts will make a difference beyond measure. Times ahead may get harder than ever, but remember that we are all in this together!

Keep your hopes up and continue the exceptional teamwork knowing that you are part of an Unstoppable Work-Force!

It will only be a matter of time before we emerge victorious to an era of wellbeing and development.

But until then, stay safe, stay healthy.

Warm Regards,**Manas Agrawal**

CEO – Affine

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]]>The post Facial Recognition appeared first on Affine.

]]>Pushing facial recognition technology beyond conventional applications for real-time deployment on a large-scale will require overcoming numerous challenges to achieve high accuracy rates at minimal processing time, Mentioned below are some of the best practices to follow while Revolutionizing the Technology.

Email: monika.singh@affineanalytics.com

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]]>The post Bidirectional Encoder Representations for Transformers (BERT) Simplified appeared first on Affine.

]]>**Bidirectional Encoder Representations for Transformers (BERT) **has revolutionized the NLP research space. It excels at handling language problems considered to be “context-heavy” by attempting to map vectors onto words post reading the entire sentence in contrast to traditional methods in NLP models.

This blog sheds light on the term BERT by explaining its components.

BERT (Bidirectional Encoder Representation from Transformers)

**Bidirectional** – Reads text from both the directions. As opposed to the directional models, which read the text input sequentially (left-to-right or right-to-left), the Transformer encoder reads the entire sequence of words at once. Therefore, it is considered bidirectional, though it would be more accurate to call it non-directional.

**Encoder** – Encodes the text in a format that the model can understand. It maps an input sequence of symbol representations to a sequence of continuous representations. It is composed of a stack with 6 identical layers. Each layer has two sub-layers. The first layer is a multi-head self-attention mechanism. And the second layer is a simple, position-wise fully connected feed-forward network. We employ a residual connection around each of the two sub-layers, followed by **Layer Normalization**. The key feature of layer normalization is that it normalizes the inputs across the features.

**Representation** – To handle a variety of down-stream tasks, our input representation can unambiguously represent both a single sentence and a pair of sentences, e.g. Question & Answering, in one token sequence in the form of transformer representations.

**Transformers** – Transformer includes two separate mechanisms — an encoder that reads the text input and a decoder that produces a prediction for the task. Since BERT’s goal is to generate a language model, only the encoder mechanism is necessary.

**Transformers are a combination of 3 things:**

In this blog, we will only talk about the Attention Mechanism.

**Limitations of RNNs over transformers:**

- RNNs and its derivatives are sequential, which contrasts with one of the main benefits of a GPU i.e. parallel processing
- LSTM, GRU and derivatives can learn a lot of long-term information, but they can only remember sequences of 100s, not 1000s or 10,000s and above

**Attention Concept**

As you can see in the image above, attention must be paid at the stop sign. And for the text, **eating** (verb) has higher attention in relation to **oats**.

Transformers use attention mechanisms to gather information about the relevant context of a given word, then encode that context in the vector that represents the word. Thus, attention and transformers together form smarter representations.

Types of Attention:

- Self-Attention
- Scaled Dot-Product Attention
- Multi-Head Attention

**Self-Attention**

Self-attention, also called **intra-attention** is an attention mechanism that links different positions of a single sequence to compute a representation of the sequence. Self-attention has been used successfully in a variety of tasks including reading comprehension, abstractive summarization, etc.

**Scaled Dot-Product Attention**

Scaled Dot-Product Attention consists of queries Q and keys K of dimension dk, and values V of dimension dv. We compute the dot products of the query with all keys, divide each of them by √dk, and apply a SoftMax function to obtain the weights on the values.

The two most commonly used attention functions are:

**Dot-product (multiplicative) attention:**This is identical to the algorithm, except for the scaling factor of √1dk.**Additive attention:**Computes the compatibility function using a feed-forward network with a single hidden layer.

While the two are similar in theoretical complexity, dot-product attention is much faster and more space-efficient in practice as it uses a highly optimized matrix multiplication code.

**Multi-Head Attention**

Instead of performing a single attention function with dmodel dimensional keys, values and queries, it is beneficial to linearly project the queries and values h times with different, trained linear projections to dk, dk and dv dimensions, respectively. We can then perform the attention function in parallel to each of these projected versions, yielding dv-dimensional output values. These are concatenated and once again projected, resulting in the final values. Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions.

**Applications of BERT**

**Context-based Question Answering:**It is the task of finding an answer to a question over a given context (e.g., a paragraph from Wikipedia), where the answer to each question is a segment of the context.**Named Entity Recognition (NER):**It is the task of tagging entities in text with their corresponding type.**Natural Language Inference:**Natural language inference is the task of determining whether a “hypothesis” is true (entailment), false (contradiction), or undetermined (neutral) given a “premise”.**Text Classification**

**Conclusion:**

Recent experimental improvements due to transfer learning with language models have demonstrated that rich and unsupervised pre-training is an integral part of most language understanding systems. It is in our interest to further generalize these findings to deep bidirectional architectures, allowing the same pre-trained model to successfully tackle a broader set of NLP tasks.

**References:**

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]]>The post TensorFlow Lite: The Future of AI in Mobile Devices appeared first on Affine.

]]>**What is TensorFlow Lite?**

TFLite is TensorFlow’s light weight solution for mobile, embedded and other IoT devices. It can be described as a toolkit that helps developers run the TensorFlow model on such devices.

**What is the need for TensorFlow Lite?**

Running Machine Learning models on mobile devices are not easy due to the limitation of resources like memory, power, storage, etc. Ensuring that the deployed AI models are optimized for performance under such constraints becomes a necessary step in such scenarios.

This is where the TFLite comes into the picture. TFLite models are hyper-optimized with model pruning and quantization to ensure accuracy for a small binary size with low latency, allowing them to overcome limitations and operate efficiently on such devices.

**TensorFlow Lite consists of two main components:**

: that converts TensorFlow models into an efficient form and creates optimizations to improve binary size and performance.*The TensorFlow Lite converter*: runs the optimized models on different types of hardware, including mobile phones, embedded Linux devices, and microcontrollers.*The TensorFlow Lite interpreter*

**TensorFlow Lite Under the Hood**

Before deploying the model on any platform, the trained model needs to go through a conversion process. The diagram below depicts the standard flow for deploying a model using TensorFlow Lite.

**Step 1:** Train the model in TensorFlow with any API, for e.g. Keras. Save the model (h5, hdf5, etc.)

**Step 2:** Once the trained model has been saved, convert it into a TFLite flat buffer using the TFLite converter. A **Flat buffer,** a.k.a. TFLite model is a special serialized format optimized for performance. The TFLite model is saved as a file with the extension **.tflite**

**Step 3:** Post converting the TFLite flat buffer from the trained model, it can be deployed to mobile or other embedded devices. Once the TFLite model gets loaded by the interpreter on a mobile platform, we can go ahead and perform inferences using the model.

Converting your trained model (‘my_model.h5’) into a TFLite model (‘my_model.tflite’) can be done with just a few lines of code as shown below:

**How does TFLite overcome these challenges? **

TensorFlow Lite uses a popular technique called **Quantization**. Quantization is a type of optimization technique that constrains an input from a large set of values (such as the real numbers) to a discrete set (such as the integers).

Quantization essentially reduces the precision representation of a model. For instance, in a typical deep neural network, all the weights and activation outputs are represented by a 32-bit floating-point numbers. Quantization converts the representation to the nearest 8-bit integers. And by doing so, the overall memory requirement for the model reduces drastically which makes it ideal for deployment in mobile devices. While these 8-bit representations can be less precise, certain techniques can be applied to ensure that the inference accuracy of the new quantized model is not affected significantly. This means that quantization can be used to make models smaller and faster without sacrificing accuracy.

Stay tuned for the follow up blog that will be a walkthrough of how to run a Deep learning model on a Raspberry Pi 4. In the meantime, you can keep track of all the latest additions to TensorFlow Lite at https://www.tensorflow.org/lite/

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]]>The post AI in Robotic Process Automation – The Missing Link appeared first on Affine.

]]>RPA can be used to automate repetitive process-driven work with well-defined outcomes. However, there is a catch. If this is Process Automation, then why don’t we just call it Process Automation? Apart from the marketing angle of using the word Robotic in Process Automation, whoever coined the term had something beyond just process automation in mind.

The intent and endeavor to automate workflows are not new. In traditional systems, automation was achieved by software developers building a comprehensive list of APIs or Scripts to cover all pre-conceived and possible tasks. However, there was a serious drawback in this approach, it was not scalable.

In modern RPA systems, instead of writing a finite number of scripts, the software systems trained to understand any number of steps as executed by recording the actual process and then replicating the same as it was recorded with the RPA platform. The 90’s generation that used excel extensively to write macros to record a set of standard activities and then store it so that every time those set of processes are run, it executes a complex but well-defined set of processes in a certain sequence. This was also called an excel macro. The current RPA platforms execute on similar principles, although at a much larger scale of complexity and size with advanced technology.

While this overcomes the problem of scale, there was still one last challenge. The significance of the word Robotic comes from the fact that there is an element of intelligence expected from the process of automation undertaken by an RPA platform. This intelligence allows the platform to take autonomous decisions based on trigger. While the trigger can be programmed, the same is not the case with the decision tree that activates the trigger.

Most RPA software’s like UIPath, Blue Prism and Automation Anywhere have so far come out with platforms that are very good at process automation by being programmed to follow a certain set of standard processes. However, they fall short miserably when it comes to making the whole process intelligent. They fail to transcend from their platforms being Automatic to Autonomous.

Let’s illustrate this with an example. Let’s assume there is a complex senior management report that gets generated by collating some specific lines of data from various enterprise databases like Oracle, SAP, and others. After the report generation, an automated mail is sent out to 500 users with specific content to each user.

The process is repetitive because multiple reports need to be generated from multiple sources of data. This is a complex process with ginormous scales of data having a multiplier effect on each of the customized reports generated for more than 500 stakeholders which are then emailed to intended recipients.

Sounds like quite a complex process but today’s RPA platforms are equipped to handle this easily and repeat process automation with minimum errors. This does not require a lot of development on existing RPA platforms. As mentioned, it can also be easily integrated into multiple platforms mentioned earlier.

However, current implementations still fall short of one critical feature required to certify it as a true autonomous implementation.

Taking one particular use case as an example, if the RPA platform had to decide on whom to send the reports based on some critical random content of the reports which could be either a picture, text or numeric and it could appear in a random pattern, the platform would fail to do so. It will fail for the simple reason that it does not know how to detect and tackle unknown situations and the decisions thereof because it is not a part of the standard process.

Similarly, many other use cases are missing from current RPA platforms. While they claim that some of them are AI-enabled, most of them are not there yet.

The main reason for such a shortcoming is that AI is not the core of process automation developers. Naturally, it is an area they are skeptical in investing and rightfully so. It is going to be difficult for them to develop such a specialized competency. The RPA platforms should, therefore, drop the word Robotic unless their platforms are truly autonomous.

The RPA enterprise customers who see a real and large-scale implementation of their process automation platforms should work with AI service providers like Affine, to be able to add the element of intelligence and true autonomous Capabilities to the process automation already implemented.

One should not have the apprehensions of integration here because just like RPA platforms can be easily integrated into existing systems, AI modules can also be integrated into either RPA modules or to the end systems directly.

That is when RPA implementations will truly become Robotic in nature.

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