Why Edge Computing is gaining popularity

Edge Computing

Edge Computing brings computation and data storage closer to the devices where it’s being gathered, rather than relying on a central location that can be thousands of miles away. This is done so that data, especially real-time data, does not suffer latency issues that can affect an application’s performance. In addition, companies can save money by having the processing done locally, reducing the amount of data that needs to be processed in a centralized or cloud-based location.

Gartner defines edge computing as “a part of a distributed computing topology in which information processing is located close to the edge – where things and people produce or consume that information.”

Ubiquitous Computing

Ubiquitous computing is a concept in software engineering and computer science where computing is made to appear anytime and everywhere. In contrast to desktop computing, ubiquitous computing can occur using any device, in any location, and across any format.

And we are probably seeing this in our own everyday life. For example, at home, we might be using an Alexa device from Amazon or we might be using Google home. We might even have an intelligent fridge or a car we can talk to.

As companies increasingly leverage ubiquitous computing to support multiple types of applications and systems, a massive amount of data is generated for decision making. However, sending all the data to the cloud can result in latency. Edge computing can drive sub-second responses by moving both computing and data closer to the user. This will reduce latency, minimize data threats, and boost bandwidth. Here are some interesting use cases across industries:

Evolution of Computing

To understand Edge Computing, we need to travel back a few decades and see how Computing has evolved in the past 50 years. The below picture provides a quick recap of the evolution of Computing.

How Edge Computing works

Edge computing was developed due to the exponential growth of IoT devices, which connect to the internet for either receiving information from the cloud or delivering data back to the cloud. And many IoT devices generate enormous amounts of data during the course of their operations.

Think about devices that monitor manufacturing equipment on a factory floor or an internet-connected video camera that sends live footage from a remote office. While a single device producing data can transmit it across a network quite easily, problems arise when the number of devices transmitting data at the same time grows. Instead of one video camera transmits live footage, multiply that by hundreds or thousands of devices. Not only will quality suffer due to latency, but the costs in bandwidth can be tremendous.

Edge-computing hardware and services help solve this problem by being a local source of processing and storage for many of these systems. An edge gateway, for example, can process data from an edge device and then send only the relevant data back through the cloud, reducing bandwidth needs. Or it can send data back to the edge device in the case of real-time application needs.

These edge devices can include many different things, such as an IoT sensor, an employee’s notebook computer, their latest smartphone, the security camera, or even the internet-connected microwave oven in the office break room. Edge gateways themselves are considered edge devices within an edge-computing infrastructure.

Why does Edge Computing matter

For many companies, the cost savings alone can be a driver towards deploying an edge-computing architecture. Companies that embraced the cloud for many of their applications may have discovered that the costs in bandwidth were higher than they expected.

Increasingly, though, the biggest benefit of edge computing is the ability to process and store data faster, enabling more efficient real-time applications that are critical to companies. Before edge computing, a smartphone scanning a person’s face for facial recognition would need to run the facial recognition algorithm through a cloud-based service, which would take a lot of time to process. With an edge computing model, the algorithm could run locally on an edge server or gateway, or even on the smartphone itself, given the increasing power of smartphones. Applications such as virtual and augmented reality, self-driving cars, smart cities, even building-automation systems require fast processing and response.

Computing as close as possible to the point of use has always been important for applications requiring low-latency data transmission, very high bandwidth, or powerful local processing capabilities — particularly for machine learning (ML) and other analytics.

Here are some interesting use cases across industries:

Use Case (a) Autonomous vehicles

One of the leading current uses is for autonomous vehicles, which need data from the cloud. If access to the cloud is denied or slowed, they must continue to perform; there is no room for latency. The amount of data produced by all sensors on a vehicle is prodigious and must not only be processed locally, but anything sent up to the cloud must be compressed and transmitted on an as-needed basis to avoid overwhelming available bandwidth and taking precious time. IoT applications in general are important drivers of edge computing because they share a similar profile.

Use Case (b) In-hospital patient monitoring

Healthcare contains several edge opportunities. Currently, monitoring devices (e.g. glucose monitors, health tools, and other sensors) are either not connected, or where they are, large amounts of unprocessed data from devices would need to be stored on a 3rd party cloud. This presents security concerns for healthcare providers.

An edge on the hospital site could process data locally to maintain data privacy. Edge also enables right-time notifications to practitioners of unusual patient trends or behaviours (through analytics/AI), and the creation of 360-degree view patient dashboards for full visibility.

Use Case (c) Remote monitoring of assets in the oil and gas industry

Oil and gas failures can be disastrous. Their assets, therefore need to be carefully monitored.

However, oil and gas plants are often in remote locations. Edge computing enables real-time analytics with processing much closer to the asset, meaning there is less reliance on good quality connectivity to a centralized cloud.

Privacy and Security

However, as is the case with many new technologies, solving one problem can create others. From a security standpoint, data at the edge can be troublesome, especially when it’s being handled by different devices that might not be as secure as a centralized or cloud-based system. As the number of IoT devices grows, it’s imperative that IT understand the potential security issues around these devices, and make sure those systems can be secured. This includes making sure that data is encrypted, and that the correct access-control methods are implemented.

What about 5G

Around the world, carriers are deploying 5G wireless technologies, which promise the benefits of high bandwidth and low latency for applications, enabling companies to go from a garden hose to a firehose with their data bandwidth. Instead of just offering faster speeds and telling companies to continue processing data in the cloud, many carriers are working edge-computing strategies into their 5G deployments to offer faster real-time processing, especially for mobile devices, connected cars, and self-driving cars.

The Future of Edge Computing

Shifting data processing to the edge of the network can help companies take advantage of the growing number of IoT edge devices, improve network speeds, and enhance customer experiences. The scalable nature of edge computing also makes it an ideal solution for fast-growing, agile companies, especially if they are already making use of colocation data centers and cloud infrastructure.

By harnessing the power of edge computing, companies can optimize their networks to provide flexible and reliable service that bolsters their brand and keeps customers happy.

Edge computing offers several advantages over traditional forms of network architecture and will surely continue to play an important role for companies going forward. With more and more internet-connected devices hitting the market, innovative organizations have likely only scratched the surface of what’s possible with edge computing.

Multi-horizon Quantile Time Series Forecasting Model

We are happy to announce our new deep learning multi-horizon time series forecasting model, which is part of our Decision Support Platform makedecision.ai. Makedecision.ai is a sales forecasting and recommendation platform that helps companies forecast sales and revenues, and suggests a couple of approaches increase your sales. Business owners can also track and understand customers’ churn and improve customers’ retention, along with offering possible key-actions for specific products sold by the company.

This is a non-technical post where we announce the model and we will submit another technical post that explains the model in detail in the coming days.

The model we have developed outperforms Facebook Prophet model by 73% and Amazon DeepAR by 46% using a normalised quantile loss. Below is a comparison conducted on a dataset for an e-commerce retailer in the UK to forecast daily revenue for the next month.

The above shows how our model visually outperforms other models mainly on how accurate the fit is and the ability to follow the spikes in the series. Below is a combined forecasting plot for all models to easily compare each model’s errors

Below is a Line plot for the whole time-series dataset used in the experiment where the hold-out set (Test set) starts at the beginning of November.
The results of median prediction evaluation show that our model outperforms Prophet by 73% and DeepAR by 46% using a normalised quantile loss.

The model is part of our effort to build a Sales recommendation system named “makedecision.ai”. You can visit https://makedecision.ai to know more and sign up for the beta version.

How Artificial Intelligence is Transforming Modern Marketing

Are you struggling to choose the best marketing strategy or measure the effectiveness and adequacy of your marketing campaign? You are not alone I’m too.

I’m no expert in marketing strategies so to set this straight before you go ahead and read the entire article, but I’m an expert in digital transformation and building intelligent systems that can advance your marketing strategy.

Today, most organizations follow a conventional and traditional approach to develop their marketing strategies. It involves a great deal of effort and requires good study of the market and alignment with the cooperate strategy. However, I would argue that these strategies are predominantly based on past experience and little to do with “your data”. It is rare to see organizations employ advanced analytics to build their strategies. Mostly, due to technical complexities or inability to harvest the data.

Data-Driven Organization

You must have seen this title before. Numerous organizations like to put this title in their strategies to indicate the organization puts data first. Although, this is a great direction to take, however, few organizations do manage to perfectly implement. Only those who really understand how to put “Data-First” manage to succeed in building a data-driven organization.

Building a “Data-Driven Organization” is a rather extremely challenging task. It would take the entire organization to achieve it. Many processes need to be redesigned, rules need to be rewritten and business logic needs to be rethought. Equally, the IT infrastructure needs to be ready to help achieve that from building systems to storing and manipulating data.

Data and Marketing

No matter how good and robust your strategy is, it will be extremely fragile if not based on facts and data. Strategy after all is a process; a thoughtful process; you need to collect data about your organization, products, customers, partners in order to tailor the strategy to work best for you.

The data is available in two places. One within your organization’s systems and the other outside your perimeters. The latter is mostly found in open data. Nowadays, social media and global news on the internet represent a big portion of that data. That is why organizations these days use social media monitoring tools to monitor and observe what people are exchanging about them and their brands.

Social Media and Marketing

Companies today are in a race to attract more customers and promote their products to consumers online and most specifically over social media platforms. It has become a practice to analyze what people say over social media platforms to measure the performance of the marketing and communication department. It’s really such a powerful tool and we have seen the impact they present on the social, economic and political life we have today.

Artificial Intelligence and Marketing

Artificial Intelligence was introduced to solve the inability to process a massive amount of data and spot important things like when people are happy or angry about a service or a product we have. Many tools today offer basic to advanced Natural Language Processing to read the unstructured data make sense of it and present insight that could help organizations improve their services.

AI can be used in various marketing scenarios and I will give a shortlist of potential scenarios where AI can be of help

  • Personalized Recommendations: AI can be used to help deliver personalized content and therefore improve the chance customer click or choose a product or service. With proper data planning, you can collect information about your customer preferences (with consent) and display the relevant products and services.
  • Customer Care: Customer care is a big umbrella that covers interacting with customers, receives feedback and process customers’ requests. AI can be used in various touchpoints within the customer journey.
  • Conversational Agents (Basic & Advanced Chatbots): Chatbots and conversational agents are becoming more and more widely accepted due to the high adoption by many organizations.
  • Content & Website Design: Today, there exist many tools that help in content generation and website designs recommendations. Organizations can easily leverage these tools to easily create and publish compelling contents.
  • Advertisement Bidding: AI is used in all advertisement platforms and organizations can use these available features. For example, you can let google ads decide what best work for you! And without the need to understand how bidding strategies work.
  • Understand Buyer Persona: Understand the buyer persona is key. You can use AI to determine the “intent” of the prospect request and then deliver the request to the right team.
  • Audience Targeting: You can use analytics and advanced analytics to determine the right target audience. You can also use AI tools to screen the public data and generate insights that can help you define your target audience.
  • Topic & Title Generations: Perhaps this is one of the most challenging tasks in AI and today we see quite good advancements in this field. You can generate titles and topics that attract more customers.
  • Customer Churns: Identifying the customers churn is important. You can direct certain marketing campaigns or offer discounts for customers likely to churn.
  • Lead Scoring and Health: You can use AI to assign a scoring for each lead to help sellers quality the lead. This helps optimize the quality of leads and the sales team’s ability to utilize the marketing efforts.

Marketing Recommendation Platform

Realizing the importance of digital marketing and the current gab in finding the right tools to help markers achieve better decisions. We at Noura.AI decided to build a platform that helps people working in marketing and companies make decisions with regard to the services and products they provide.

Musihb (مُسْهِب), is an advanced artificial intelligence global media platform that assists organizations in making decisions in the area of Marketing and Customer Success. Mushib collects Millions of NEWS and SOCIAL MEDIA feeds, analyzes them and provides organizations with the insights and decision choices to help optimize customer experience and improve business outcomes.

Unlike other tools, Musihb provides “recommendations” We call them “Parameterized Recommendations”. Where the AI engine determines the best recommendations and then decide the values within these recommendations that fit your organization. For example, available tools identify your negative sentiment, Musihb’s AI engine on the other hand tells you what improves your sentiment and by which percentage you will probably improve when following the recommendations!

Try it for Free!

We believe AI should be available to all. Most of the available tools are very expensive and few organizations can afford to bear the cost.

We provide a ton of features with very affordable subscriptions fee that is suitable for many. You can also try the tool before you commit to any payment. TRY NOW.

Before you invest in Artificial Intelligence WATCH THIS

Are you thinking to invest in artificial intelligence or get into the data science domain? surely, there has been so much fuzz about it in recent years, big companies and small alike are increasingly investing in these technologies, so the obvious question should you invest now? 

In this article, I’m going to shed light on why should you start to consider investing in AI and how should you approach that. Obviously, this article is not meant for everyone but even if you are not in the IT field this article will highlight why business executives should pay attention to this and how it will help them in their digital transformation journeys. 

Alright, so let me begin by attempting to convince you putting your money, time, and effort into this investment. Let’s look at some numbers here

  • In 2015, a survey by Gartner showed only 10% reported that either they use AI or thinking about using it, while data shows that number has risen dramatically in 2019 to 37%
  • In 2019 the market for Artificial Intelligence was value to about $27B with projected growth to 10X by 2027
  • According to statista.com AI contribution to GDP in 2030, by region is expected to be 26% for China GDP 14.5% for north America and 12.5% for my home country

I hope this whet your appetite to know more about the investment in AI. For that I will share with you three things I believe essential for any investment considerations and more specifically so in advanced technologies.

Start a Learning Journey

You need to familiarise yourself with data science and advanced analytics. It’s so easy these day to find good courses online both free or paid. The learning is not just for the purpose of being data scientist but rather gives you understanding of the field you are investing in. Another very important topic you need to research is the problems that you think AI would be of great help. You need to envisage how the use of advanced technologies would really solve a real business problem. In other word, you need to be the digital advisor who uses his/her creativity to solve challenging problems. Remember learning is a journey not a destination. So keep on learning, experimenting and exploring new things 

Work in the Field 

If you can afford to work in a startup or international company do so to gain experience and get exposure to the market and access a large network of customers and therefore explore various challenges.

Surely sometimes, it may not be possible to get a job in this filed, However there are other means such as freelancing and open source communities that you can leverage.

It is very crucial to be equipped with both theoretical knowledge and practical applied experience that teach you what works and what does not.

Due Diligence

This step is perhaps discussed a lot and would vary depending on how you approach the investment. So if you are investing your money in a startup then you would want to look for few things.

  • The robustness of the idea, its viability to market, visibility and impact on business and society.
  • You need to look for the Founders’ past history and current competence and skills because after all they will be leading your investment 
  • Founders readiness of vision, clarity, go to the market and operational plans are very critical. It’s very important that you look for business models that offer resilience and flexibility that can also provide diversity rather than relying on one single product or idea because that could be risky
  • look for a startup that has the right team mixture, it’s like a recipe. Every details matter. Building a thriving culture that value customer empathy and have great values is essential for any business success
  • Check for Market tractions and current customers if any. Validate how will the business model attract new customers and most importantly how fast? Again I stress on the business model and its ability to organically grow in market size and consumption

On the other hand if you are investment your time, skills and energy by beginning a startup you then need to ask yourself five questions: 

  • Am I offering a unique value proposition that solve a problem for a large business segment and there is an urgent need at this time? 
  • Do I have the capability to implement this idea, on time, at budget and offer it on timely manner and acceptable price?
  • Am I building an evolving business model that can sustain changes in market and can easily pivote and tranform to different business models?
  • Am I able to build a thriving culture that attract talents, create shared values and goals? and above all, inspire them to make the impossible?
  • Do I have what it takes to attract customers and investors and be the face and the biggest seller of the company? 

These were the three tips I wanted to share with you today. AI is all about R&D so always look for startups that profoundly exert effort into the research and development because the process always involves trial and error and results only come after many many failed experiments.

Entrepreneurial University: How to Drive Private Sector Innovation?

Think with me! How many great research ideas, papers and projects conducted by university Professors and final year Students are now “on the shelf”? How many wasted business opportunities a company has missed by not having an innovation team or department? But wait, from where great ideas come in the first place? 

As someone who worked many years as a Digital Transformation advisor, I say with certainty, business innovation comes mostly from research. In fact, big companies do have enormous R&D teams and they spend billions of dollars on Research alone. An important question would then be how the private sector and particularly startups can follow the same path?

We at noura.ai for example, work with university professors on research papers that represent “THE CORE” of our work. We firmly believe our success comes from working on the latest research in Data & AI combined with Business Innovation to create next-level products that can compete with technologically advanced offerings in the market. 

I would guess that you have been intrigued by the “Entrepreneurial University” term in the title. Did I get that right? I have always been captivated by the notion of working with universities to create entrepreneurial thinking, collaborate and solve the knowledge paradox between the academic and private worlds. In fact, this model is widely used by developed countries and considered the second source of funding for academic research in the US.

Finding Common Ground

Finding the common ground between academic researchers and private sectors can be difficult and a road full of hardship and that is mainly in my opinion due to the different mindset between sellers in private companies and scientists. Nevertheless, both parties recognize they need each other to reach their goals. So what is the secret to bridging the gap between the two fields?

The secret in my opinion is innovative thinking. Both fields can embrace innovative thinking and adopt a process. This process should serve as a “connector” between the two fields.

Private Companies Viewpoint

Private companies look for profitability and always measured by their ability to make money. Yes, there are other measures companies employ but at the very end, it is how much money they earned in a given period. So for simplicity, let say private companies do view the world from a money angle. Now, to earn that many companies must implement various strategies to better allocate resources and achieve their goals.

Depending on the company strategy and the type of products they make, traditional products are now very hard to sell. In fact, the consumer has become more sophisticated and demand new experience. Companies have no choice but to transform the way they offer business and continue to innovate. To do that, many have sought to establish an innovation department to start to ideate and bring new ideas.

It is unequivocal that basing products on the latest in research would mostly position the companies’ products as leading in the industry assuming proper marketing and sales strategies. Therefore, working with research institutes would bring great opportunity to business. Realizing its significance, private companies can work with researchers and ensure the result is aligned with the company strategy and the final product is consumer-friendly.

Researchers Viewpoint 

Researchers, on the other hand, focus on the quality of research and the outcomes. Although researchers do consider the practicality of the proposed solutions, however, they don’t tend to focus on the sell-ability of the solutions. It is immensely important that researchers are not distracted by the sales or business matters so they are focus on the quality of outcomes.

Having a clear process will certainly help both researchers and private companies collaborate and produce tangible outcomes without compromise from any party.

Process To Adopt 

Having an independent regulatory body that helps shape the regulation and guidelines that govern the relationship and ensure ethics, seemly and proper conducts are in place is of great value. There is an active relationship today between researchers and the private sector, however, this relationship is not really bound by a clear process that leaves no ambiguity. The below is a proposed process; rather than a simple one; that ensure a consistent relationship:

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Influence & Impartiality

Although the idea of Industry Research Funding seems effectively inciting to the development of research. However, there are growing concerns over the fair-mindedness of research, ethics and impartiality of both research topics and researchers. This has inclined countries to develop guidelines and governance models to help ensure the adherence to properly checked procedures to help avoid conflict of interest and keep preserving the lofty goals of scientific research while enabling the private sector to both contribute to research advancement and help bring innovation to business and consumers.

Financial Model

Perhaps, having a financial model that ensures both researchers and research institutes are compensated well is really needed. The financial model will also incentivize the private sector to invest. The cost of establishing an innovation team at the company would be higher compared to offering to compensate a researcher in a university. Furthermore, the industry research fund will help researchers produce more results. Not only that but also, it enables researchers for example access enterprise-level tools and resources. For instance, researchers can access data annotators in a company or hire someone easily via the company purchase department. They can also build appealing UI that help deliver the solution and show its capability in a better and well-presented UI.

Regulations & Guidelines

The need for setting up regulations and guidelines to govern the relationship between the private sector and the academic research institutes is unequivocally important to ensure the sustainability of the relationship and the yielded outcomes that contribute to business innovation and the increase in the research activities.

The key highlights that need to be taking into consideration whilst planning and building such guidelines and governance models need to ensure:

  • Clear guidelines for Intellectual Property and Patents ownership. This also should include any artefacts such as code and datasets
  • Clear guidelines for future development and usage of the research outcomes. This should also include any packaging and repackaging of any solution.
  • Clear guidelines on the licenses scheme and distribution.
  • Clear guidelines on the compensation scheme and governance model to ensure fairness and avoid any abuse.
  • Clear guidelines on procedures to ensure research fairness as well as correctness and preserve the ethics of research conducts.

It’s also worth it that government need to build a framework that helps both the academic and private sectors collaborate without worrying about complex engagement models and fear of preaching any law. That also should include creating a body that oversights the relationship and ensures adherence to the herewith in framework.

I hope you found this article useful and enriching and would be delighted to receive your kind comments and feedback. Also, please do share your experience if you are an academic and had the chance to work with the private sector.

AI-Infused Decision Making

Have you ever contemplated in the number of times you make decisions in a given day? It’s time consuming as well as irritating to be reluctant to purchase an item or make a request. What if you have been given a “magical” tool that could help narrow down your choices? What if we create simple tools that can adopt to your style, situation and the current context to give you more precise and accurate recommendations.

If this resonates with you then continue reading this article, as I will share some thoughts around how new type of digital systems can help you and other organisations make better decisions. 

What is AI-Infused Decision Making 

Perhaps I’m endeavoring to coin a new term in the world of the business decision making and artificial intelligence. Working for so many years in the IT field and observing numerous successful and indeed as well as failed projects, one prevalent mistake I see is when organisations think the use of AI can solve any problem and will wondrously help them sell more and market better! well, the conspicuous answer is in the negative! I shall explain.

But first let me give a definition for what do I mean by “AI-Infused Decision Making”. For so many years, people have talked about Expert Systems and how they help make better decisions. According to wikipedia, in artificial intelligence, an expert system is a computer system emulating the decision-making ability of a human expert. The expert system is fed a set of knowledge base with the aim to solve complex problems by applying rule based systems. AI Decision Support systems on the other hand are systems that aid in making decisions. Normally, they provide means for users to interact and use the system to reason and finally conclude.

So what is AI-Infused Decision Making? it’s simply put a combination of both Expert Systems and the use of advanced sophisticated methods in decision support systems to help guide users reach possible decision choices. 

Now that we have defined the term, let us dive into why I believe it provides distinct view and perspective of how new Decision Support Platforms are made and how it contributes into transforming how organisations make strategic and operational decisions. Not just that, even on the personal level, many of us make decisions with the help of mobile or wearable devices. Hence, how significant making decision is! 

Successful Transformation = Taking the Right Decisions on the Right Time 

I would presume many would to concur that making a successful transformation is profoundly correlated with taking the right decisions on the right time. Numerous groundbreaking transformative (ideas, projects, products, etc.) were not completely novel or original but rather composed and packaged entirely in a manner that is truly exhibit solving real and challenging business problems.

So take a moment and think for a second. How many of us when he/she sees a successful business; say I thought about this many years ago? is not that right? well, what went wrong? why didn’t he or she started that business? Was it a lack of will? Perhaps, but I would reckon it’s to do with “Uncertainty“. Our fears of uncertainty heavily influence our decisions. You, for example, made a decision to read so far! if you were uncertain and skeptical about the value of reading this article you would not have spent time thus far reading.

Dealing with Uncertainty? 

We live in a world that is for the most part uncertain, nevertheless we thrive to seek certainty, because certainty gives us confidence in the validity and effectiveness of our decisions. Consequently, most of the organisations look for visionary and talent people who can see through future and anticipate opportunities and make “The Right Decision On the Right Time”.

For so many years, IT companies have sought to create tools to help professionals better make decisions. From automations to the most advanced technologies and use of Machine Learning and to help solve complex problems and provide better recommendations.

A Proposed Model to Solve Uncertainty 

Let us take a logical example of how most people would go about making a decision. Let’s say you want to buy a car. First you will go to the web and search for the car spec, features, reviews, pros and cons, …etc. Then you may contact a subject matter expert for consultation or a friend who happens to own one. Then finally, you look at the available data like cars sales, parts costs ..etc. Isn’t that a very logical approach?

This is precisely what I’m proposing here is to build an AI-Infused system that takes into consideration the previously described approach. The proposed platform will utilise the leading-data found on the Internet and source all reported news and social media feeds, including customer reviews and ratings. This measure represents the uncertainly observed on the social media platform likely to represents people’s views. Incorporate that with Subject Matter Experts Opinion using methods such as Delphi method to source feedback. After that, combine it with lagging-data such as company’s sales, past deals ..etc. The common methods of forecasting depend on choosing internal factors that are often available to the companies or service providers, such as prices, daily sales, etc. However, this method relies on combining the previously mentioned method with inputs from open data such as people’s sentiments about a product or the popularity of a product or company. As well as integrating economic indicators in the forecasting process and enhancing it with the participation of experts such as SMEs. Integrating all of these inputs into a deep learning-based system in an effort to give a more accurate prediction and forecasting than the currently available techniques.

To read and know more about this approach click here

* The images in this article are licensed under Envato Elements

noura.ai, the decision company

I have always been passionate about technology and what it can do to transform business. Similarly, I was very fortunate to have had the chance to work in international company and meet many customers in the middle east. During that time it was obvious to me that the region is in very need for a private AI research company that attempt to bridge the gap between research institutes and private sector. Truth be told, it isn’t simply remarkable to the Arab world but indeed for the whole world. That is why big tech companies are in race to build the most advanced AI platforms.

So, noura.ai was born to partner with researchers and private sector to build the most comprehensive AI decision support platform. noura.ai is a young research-led startup company that aims to solve a broad range of decision problems. We believe succeeding in digital transformation requires blurring the divide between academic research and business innovation.

Four research areas. One goal: create an AI decision platform

Supercharging the decisions making backed with four powerful research areas that make you ready for competitively by affect and manage outcomes and reduce risks.

Deep Reinforcement Learning (DRL): Build adoptive models to predict consequences of behavior via interaction with environment. The goal is to create an incentivized agents capable of making and evolving decisions.

Probabilistic Modeling: When making decisions in complex and uncertain environment machine learning algorithms alone are not sufficient. This is where probabilistic models are needed to either forecast or support other machine learning algorithms in making better predictions.

Multi Agent Systems (Game Theory): To reach out to the best possible decision an agent based system compete or collaborate to optimize the recommendation using game theory.

Econometrics: Machine learning algorithms are not built to deal with causalities and causal inference as econometric models do. We combine machine learning algorithms with econometric models to help understand economic and policy uncertainties.

I’m very excited about the challenges and opportunities ahead and I look forward to work with our customers and partners to transform their business.