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The pandemic crisis has demystified a long-drawn conclusion of work from office holding an edge over remote work. Organizations globally are prioritizing their investments in digital workplace transformation mapping to the work from home or hybrid work environment. Enterprises will need to focus on flexibility and agility in response to adapt to a fast-changing situation.

Before the COVID-19 pandemic, while there was a digital-first slogan everywhere, transformation lacked the front seat counting on the priority investments then. Several legacy applications and processes took a hit during the pandemic lockdown due to a lack of scalability. Additionally, with an increase in employees working from home, communication and engagement became an additional challenge.

Slowly organizations looked at AI as a game-changer. Automation and AI are at the forefront of managing these hurdles efficiently in addition to enhancing stakeholder experiences.

AI and ML are helping enterprises globally to adapt to new ways of working and scaling businesses, especially in today’s crisis. In the COVID-19 era, organizations that successfully combine human and machine capabilities have not only survived the tide but were able to tweak the tide to an opportunity.

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Infusing AI in transforming legacy applications has given a competitive advantage. According to Forbes columnist Louis Columbus, 63 percent of enterprise leaders say they have adopted AI technology to catch up with rivals. Having a plan to adopt AI isn’t just about working better internally; but also not being left behind.

Artificial Intelligence In The Digital Workplace

AI covers a broad and continually evolving range of technologies in the modern digital workplace. Everyday AI brings substantial importance to the organization, transforming the way organizations communicate, collaborate, search, and share information in the digital workplace.

“Over the next decade, AI will offer employees unprecedented information awareness and insight, freedom from low-value-additional activities, and the ability to use complex technologies,” says Bern Elliot, Distinguished VP Analyst, Gartner.

The future of AI involves evolved cognitive systems, advanced algorithms, and anomaly detections. Let us explore some areas for consideration in an AI-infused digital workplace.

AI-Powered Personalization

Employees spending a lot of productive hours in finding relevant information on time due to information overload is an age-old hassle awaiting automation. Personalized content delivery is the key to the intranet adoption in the organization. Modern intranet equipped with AI recommends content based on a user profile, interactions, teams, etc. With AI tapping into the legacy applications, employees get tailored and relevant information quickly, based on their activities, interests, and locations, which not only saves time and effort but also leaves more productive time for skilled work.

As a Forrester report suggests, More than half (54%) of global information workers are interrupted from their work a few times or more per month to spend time looking for or trying to get access to information, insights, and answers. All the information is generally available within the organization but is not easily accessible or searchable at the right time.

A possible and positive side effect of AI-powered personalization is adoption and enhanced employee engagement. Increased engagement rates are a delight to every HR personnel in this crisis.

Personalization is the key, making employees more receptive to intranet adoption and utilizing the information hosted on it. As expectations from the intranet evolve rapidly to address emerging needs, organizations are transforming from using the traditional intranet to leveraging AI, machine learning, and contextual processing. These changes have introduced elements of personalization in today’s digital workplace equipping employees with the right information at the right time.

Knowledge Mining

Employees spend a maximum of their time either looking through or creating unstructured information. A lot of information is just locked away in thousands of files, which is difficult to find and time-consuming as well. By the time, the information is identified and organized, it loses its value and is fit for only archiving.

Almost 80% of the data generated is unstructured, and much of it can only be interpreted manually by an expert. Knowledge mining techniques using a combination of AI services navigate content understanding over immense amounts of unstructured, semi-structured, and structured data that allows the organization to uncover insights, explore it, and find relevant information quickly. Auto-tagging and indexing make the documents discoverable but also enhances its searchability and accessibility.

Right now, when every knowledge worker is figuring out how best to stay in line with organizational wealth, there is no better time than now to start with AI in knowledge mining.

Cognitive Enterprise Search

Keeping remote employees both engaged and productive, however, will be particularly challenging for companies. Organizations that are still using the old keyword-based enterprise search engines of the past are now obsolete. Cognitive search is the new technological advancement in enterprise search that uses artificial intelligence (AI) technologies like NLP, ML to ingest, analyze, and organize digital content. Leveraging these technologies facilitate search results that are more relevant and contextual, eliminating the frustration and recreating near duplicates of files.

With AI algorithms and office graph kinds of technologies, the search mechanism is now well equipped in establishing the context of the search query or searcher’s intent. Accordingly, fetches search results, which are per user’s expectations.

Besides, AI-driven search solutions are a powerful way for enterprises to seamlessly integrate the knowledge from different platforms and make it readily available to their employees in a single user interface.

The single search feature eliminates the necessity of switching between numerous apps that eliminates time wasted on tasks like re-entering credentials multiple times. Moreover, unified data tools allow enterprises to streamline their business processes.

Virtual Digital Assistants

In the current remote working scenario, where employees are managing personal activities like house chores, kids, and pets alongside their office duties, work timings of employees are beyond the typical 9 to 5 work hours. Therefore, the need for assistance round-the-clock to provide instant resolutions to employee support requests is much stronger.

With AI making inroads, regular support requests wait for an hour is quickly resolved by AI chatbots. The inherently complicated request, requiring human intelligence will quickly escalate to human agents owing to one of the great features of virtual assistants — the human handoff feature.

AI-powered chatbots equip employees with more personalized and relevant information through a conversational interface from anywhere at any time. And also update employees with the latest updates and news. These processes will improve employee productivity and streamline business operations in the new normal.

One of the key use cases of virtual agents in the new normal is for the HR department. They are using it to collect health information, create a regular employee touchpoint, distribute health and safety bulletins, and much more.

Conclusion

Looking at the realms of possibilities, it would not be an exaggeration to say that an AI-infused digital workplace can act as a catalyst to bring a fundamental shift in the traditional workplace environment. An AI-Powered autonomous digital intranet acts as the foundation of the digital workplace. It is a one-stop-shop to collaborate, communicate, and share information enabling organizations to boost intranet adoption, employee engagement, and productivity.

A digital workplace without an intranet as its foundation will end up being a collection of disconnected digital waypoints. But with an intranet anchoring the digital workplace, the organization will be laying the foundation for success.

Mesh 3.0 launched as the World’s 1st AI powered autonomous SharePoint Intranet helps organizations in unlocking untapped knowledge, streamlining processes, and creates a seamless yet effective global network — providing a central hub for breaking down silos. Mesh modern intranet features enhance employee engagement, boosting internal communication and collaboration that catalyze a digital workplace environment.

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How To Revolutionize Your Digital Workplace With AI 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-to-revolutionize-your-digital-workplace-with-ai-b0acb0008864?source=rss—-5e5bef33608a—4

Convolutional Neural Networks are very similar to ordinary Neural Networks. They are made up of neurons that have learnable weights and biases. Each neuron receives some inputs, performs a dot product, and optionally follows it with a non-linearity. The whole network still expresses a single differentiable score function: from the raw image pixels on one end to class scores at the other. And they still have a loss function (e.g. SVM/Softmax) on the last (fully-connected) layer and all the tips/tricks we developed for learning regular Neural Networks still apply.

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So What’s New in ConvNet

ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture. These then make the forward function more efficient to implement and vastly reduce the number of parameters in the network.

Regular Neural Nets

Neural Networks receive an input (a single vector), and transform it through a series of hidden layers. Each hidden layer is made up of a set of neurons, where each neuron is fully connected to all neurons in the previous layer, and where neurons in a single layer function completely independently and do not share any connections. The last fully-connected layer is called the “output layer” and in classification settings, it represents the class scores.

The CIFAR–10 dataset consists of 60000 32×32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.

Regular Neural Nets don’t scale well to full images. In CIFAR-10, images are only of size 32x32x3 (32 wide, 32 high, 3 color channels), so a single fully-connected neuron in a first hidden layer of a regular Neural Network would have 32*32*3 = 3072 weights. This amount still seems manageable, but clearly, this fully-connected structure does not scale to larger images. For example, an image of a more respectable size, e.g. 200x200x3, would lead to neurons that have 200*200*3 = 120,000 weights. Moreover, we would almost certainly want to have several such neurons, so the parameters would add up quickly! Clearly, this full connectivity is wasteful and the huge number of parameters would quickly lead to overfitting.

3D volumes of neurons. Convolutional Neural Networks take advantage of the fact that the input consists of images and they constrain the architecture in a more sensible way. In particular, unlike a regular Neural Network, the layers of a ConvNet have neurons arranged in 3 dimensions: width, height, depth. (Note that the word depth here refers to the third dimension of an activation volume, not to the depth of a full Neural Network, which can refer to the total number of layers in a network.) For example, the input images in CIFAR-10 are an input volume of activations, and the volume has dimensions 32x32x3 (width, height, depth respectively). As we will soon see, the neurons in a layer will only be connected to a small region of the layer before it, instead of all of the neurons in a fully-connected manner. Moreover, the final output layer would for CIFAR-10 have dimensions 1x1x10, because by the end of the ConvNet architecture we will reduce the full image into a single vector of class scores, arranged along the depth dimension. Here is a visualization:

Left: A regular 3-layer Neural Network. Right: A ConvNet arranges its neurons in three dimensions (width, height, depth), as visualized in one of the layers. Every layer of a ConvNet transforms the 3D input volume to a 3D output volume of neuron activations. In this example, the red input layer holds the image, so its width and height would be the dimensions of the image, and the depth would be 3 (Red, Green, Blue channels).

A ConvNet is made up of Layers. Every Layer has a simple API: It transforms an input 3D volume to an output 3D volume with some differentiable function that may or may not have parameters.

Layers used to build ConvNets

As we described above, a simple ConvNet is a sequence of layers, and every layer of a ConvNet transforms one volume of activations to another through a differentiable function. We use three main types of layers to build ConvNet architectures: Convolutional Layer, Pooling Layer, and Fully-Connected Layer (exactly as seen in regular Neural Networks). We will stack these layers to form a full ConvNet architecture.

Example Architecture: Overview. We will go into more details below, but a simple ConvNet for CIFAR-10 classification could have the architecture [INPUT — CONV — RELU — POOL — FC]. In more detail:

• INPUT [32x32x3] will hold the raw pixel values of the image, in this case, an image of width 32, height 32, and with three color channels R, G, B.
• CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and a small region they are connected to in the input volume. This may result in volume such as [32x32x12] if we decided to use 12 filters.
• RELU layer will apply an elementwise activation function, such as the max(0,x)max(0,x) thresholding at zero. This leaves the size of the volume unchanged ([32x32x12]).
• POOL layer will perform a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12].
• FC (i.e. fully-connected) layer will compute the class scores, resulting in a volume of size [1x1x10], where each of the 10 numbers corresponds to a class score, such as among the 10 categories of CIFAR-10. As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.

In this way, ConvNets transform the original image layer by layer from the original pixel values to the final class scores. Note that some layers contain parameters and others don’t. In particular, the CONV/FC layers perform transformations that are a function of not only the activations in the input volume but also of the parameters (the weights and biases of the neurons). On the other hand, the RELU/POOL layers will implement a fixed function. The parameters in the CONV/FC layers will be trained with gradient descent so that the class scores that the ConvNet computes are consistent with the labels in the training set for each image.

Summary:

• A ConvNet architecture is in the simplest case a list of Layers that transform the image volume into an output volume (e.g. holding the class scores)
• There are a few distinct types of Layers (e.g. CONV/FC/RELU/POOL are by far the most popular)
• Each Layer accepts an input 3D volume and transforms it into an output 3D volume through a differentiable function
• Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL don’t)
• Each Layer may or may not have additional hyperparameters (e.g. CONV/FC/POOL do, RELU doesn’t)

The activations of an example ConvNet architecture. The initial volume stores the raw image pixels (left) and the last volume stores the class scores (right). Each volume of activations along the processing path is shown as a column. Since it’s difficult to visualize 3D volumes, we lay out each volume’s slices in rows. The last layer volume holds the scores for each class, but here we only visualize the sorted top 5 scores and print the labels of each one.

In the next article, we will discuss the individual layers and the details of their hyperparameters and their connectivities.

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Don’t forget to give us your ? !

Convolutional Neural Networks (CNNs / ConvNets) for Visual Recognition 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/convolutional-neural-networks-cnns-convnets-for-visual-recognition-cae879a70f1a?source=rss—-5e5bef33608a—4