Uses of Virtual Reality in Warehouses

Artificial Intelligence 

Virtual reality is an immersive desktop interface which happens in a virtual world. You’re capable of communicating with actual physiological situations. Most frequently utilized specific representations of Virtual reality are the headphones which carry users to diverse perspectives. Devices produce accurate representations, noises, as well as other stimuli that mimic the overall behavior of the participant. Thus, using a helmet allows the consumer a freedom to move and glance about in this immersive environment, while in fact they’re wandering inside of their dining area.

Artificial Intelligence improving firms

These modern and evolving innovations are a fetching key element for several companies. This has the ability to enhance support for customers, decrease operational expenses, assist with protection and enforcement, and strengthen daily operations. If you’d like to keep control of certain rivals, you may bring augmented reality into effect in the office.

In illustration, augmented reality operates instantaneously and has been utilized by several workers simultaneously. When the software system is known, this interface allows acceleration in communications and provides producers and distributors visibility into organizational performance growth.

Here are several aspects in which augmented reality is applied to improve the activities of your business.

Attracting New Consumers

Consumers can join the company remotely by linking to every good. It enables brands, including particular merchants, an incredible incentive to move their company to another stage. Clients will soon get a unique opportunity to communicate with goods from the convenience of one’s house.

Gathers Useful Information

Augmented reality monitors and analyses every behavior, gesture and content behavior at several moments. The advantage of the company is that it can learn what the people thought and find the goods individuals become involved in. It provides the company with the chance to deliver personalized deals based on your tastes. In reality, augmented reality converts any interaction towards details for the company.

Enhancing Interaction

Utilizing augmented reality, conversations regarding the warehousing in the enterprise are enhanced. Every other worker has next to no background for such issue.  As more of a consequence, any individual might pry too closely at the issue in an augmented world. This is the product of an active and meaningful debate.

Adding more pixels and improving VR devices

The advent of artificial intelligence and premium screens is only two technologies that drive—and extend smartphone business. During an attempt to produce genuinely interactive device VR, a proper combination of maximum quality and large display speeds is required. Using a quicker, pixel-rich monitor is an upcoming move, but it would need additional energy modules to operate it. The smartphone community has to take action to address the power consumption dilemma enough so cellular VR can truly benefit.

The range of reports and events surrounding augmented world keep rising. Last year’s CES and Cell World Congress activities demonstrated the substantial advances achieved in each of these VR devices and network. Although it appears like everybody is getting on the VR platform, the standard of the VR will differ significantly dependent on the quantity of equipment or the amount of exposure.

Elevated VR systems like Oculus Rift and HTC Vive definitely get the greatest prospects for high level of simulations thanks to its capacity to exhibit good equipment; however we agree that almost all users would probably encounter VR for the very first moment on a smartphone device due to various reduced costs and easy connectivity. To leverage on this phenomenon, design and manufacturing development team works on enhancing many facets of smartphone VR interface, namely HMD devices, smartphone VR remotes, android application, and VR material.

Moving to Real Interactive Smartphone VR

In order to produce fully interactive digital VR—that it exploits the recipient’s imagination to think they’re anywhere different than where they really are—there must be an ideally matched combination of multiple additives: full definition, good image filling size, faster refresh time and better battery. Plainly defined, traditional digital screens may not have enough quality or high update speeds to offer a decent VR.

One of the greatest problems confronting VR is to solve its “Screen Door Effect” (SDE) in which the audience will clearly see the blemishes dividing the frames. This dilemma exists since Wearable devices utilize mirrors to amplify the images of a monitor in a much broader line of sight.

The requirement for more dimensions and higher screen resolution

Including further pixels is the very first step towards addressing the SDE query. Most of today’s premium VR-enabled devices have a maximum output of 500-600 pixels per inch (PPI). Stuffing additional pixels through each centimeter will overcome the immersive gameplay impact and therefore have a better value picture, owing to the fact that VR implementations cut the amount of pixels in quarter to fit every specific object. The makers of displays well acknowledge the possible promise of VR and the need for maximum magnification. Japan display maker Japan Display, a partnership among Sony, Toshiba and Hitachi, has officially opened proposals for a VR-focused handset showcase with an outstanding 800PPI. Likewise,

Virtual Reality in the Cinematic Universe

The film version, “Ready Player One,” has given way to several intriguing concepts. In the face of the dystopian images of innovation and the business environment that had gotten out of control in 2045, one issue appeared to hit a nerve of viewers.  Nah, not the essence of human personalities, and not the potential dark world that faces us—they have always been about however cool the augmented worlds technology looks. Advanced Virtual reality goggles have made it possible for protagonists to connect to artificial environments in amazing respects.

Dream of how such an innovation will have an effect on the movie business soon. Should we now have to delay three decades to utilize this type of device?

Truthfully, we’re way better equipped to make this innovation a possibility than you may expect. Artificial intelligence has been changing rapidly in the last decade, yet only avid players and gadget nerds are pushing market acceptance. The movie business may be somewhere on the brink of some big, thrilling modifications due to Virtual reality.

Augmented World: A Condition of Virtual Reality Software Nowadays

Virtual Reality is now swishing in a variety of areas. In attempt to comprehend where Virtual reality software could impact cinema, it benefits to get a clearer sense about how some sectors are being influenced. It covers computer plays, Television programs, as well as film apps. The aspects of performance generated and the development of businesses revolutionizing in Virtual reality devices are influenced by the popularity of Virtual reality data. Let us just drill down at that channel:

Television Programs

The idea of a Virtual Realty based Television program appears odd, but it’s a fully engaging and exciting style. If you’d like to have an understanding about how this operates, just go the internet; a number of Livestream, Dailymotion, and Augmented reality Television content could be watched utilizing this device. Several creators are producing such completely-fledged Television programs.

The whole idea has been adopted in innovative forms among artists. Becoming ready to migrate into the boots of a protagonist in a continuous storyline seems to have the ability to become a strong and entertaining lure to consumers. Though the total revenue is limited at the present time, everybody is not willing to trade personal audio system seats for a free of hurdles Virtual reality area, rising increasingly and seems to have the ability to be a big priority for businesses.

Implementations outside Media

There exist a number of non-entertainment applications that display the strengths of Virtual reality, and it is necessary to examine them while evaluating the potential of the application. Stated under are a few instances:

Multiple organizations are already leveraging Virtual Reality based teamwork tools to coordinate knowledge and interact with colleagues through three dimensional animations.

Teachers also started using Virtual reality to encourage learners to respond in immersive environments as well as to undertake “aerial views” of places of important monumental relevance.

Doctors use Virtual Reality goggles, together with tactile technologies, to study surgical techniques via interactive experiences.

Singers and graphic designers need exposure to a variety of Virtual reality tools which enables them to create original artworks inside a virtual era.

Digital technology is obviously now an exceedingly central characteristic of ordinary routine. Such developments are important as they raise the understanding and rate of technological change, with this follows the enhanced possibility of performance in the field of cinema.

Films

In much the same manner as Virtual reality TV series, this technique is often utilized to create immersive encounters. Global actors provide a logical move further in IMAX software that has contributed significantly in cutting-edge cinema for the last several years. yet in reality, IMAX has indeed embraced Virtual reality, establishing IMAX Virtual reality centers throughout the globe. It is often regarded strictly mostly as creative pursuit. Over the last few centuries, movie conventions (including Sundance, Tribeca and Venice) also started to integrate Virtual reality into their sessions.

Even then, like several businesses, Virtual reality still isn’t common though. Vanguards are trying to expand this. For instance, developers at Protozoa Pictures are developing a hundred thousand series on the discovery of nature named ‘Spheres.’ It raises the question: ‘Can artificial intelligence substitute conventional theater?

AR/VR in Learning

have the tremendous potential in the today’s learning landscape. Harnessing the true potential of these two technology, a student can nourish their thirst of knowledge in a better and an effective way.

Whether you are a tech-savvy entrepreneur, project director, student, or an employee of any multi-national company, you can unleash AR/VR technology to learn plethora of different things.

Nowadays, the spread of the pandemic has devastating impact on all the sectors including education. COVID-induced isolation didn’t hinder any teacher from delivering a math or science lecture to his/her students. This is where the significance of AR/VR in the learning field comes into play.

Embracing AR in the Learning

VR offers countless learning opportunities to a student or a professional engineers. Leveraging the power of VR, a structural engineering student can visualize the complete design of Eifel Tower and its underlying construction methodology.

Dr. Narendra Kini, CEO at the Children’s Health System, has found the profound impact of VR when she used it to teach her medical student. She asserted that when she used multimedia, power point slides, and in-class lectures to teach her students, she found that the retention rate was merely 20%. This means students forgot 80% of the lecture content.

After incorporating VR into her medical training, she experienced an exceptionally high retention rate of 80%. That’s a whopping 400% increase in the learning experience.

Significance of AR and VR in the Learning Field

As of today, companies around the world are reaping the benefits of AR and VR to streamline the learning process in schools, colleges, universities, and industries like real estate, biomedical research, medical image processing, and automobile engineering.

Statistics revealed by the markets, investment in augmented reality and virtual reality in the learning field is likely to grow from USD 9.3 billion in 2018 to USD 19.6 billion by 2023.

Traditional learning institutions like schools and universities are increasingly looking for the better technologies to transform the traditional learning practices. Technology-driven learning and smartboards can replace the traditional chalks, textbooks, and blackboards.

How AR and VR can revolutionize the Learning Environment?

Augment and Virtual reality has the potential to spur the growth of tech-driven EdTech startups. Before the advent of AR and VR, technology plays a significant role in revolutionizing the education with an aid of interacting tools like digital content, smart classroom, and online assessments.

Implementing AR and VR in learning environment can open a door of whole lots of opportunities for both the students and teachers.

Here are some compelling reasons how AR and VR can revolutionize learning:

  • Enhance the student’s participation in the learning process.
  • Escalate student’s learning potential
  • Allow students to visualize some vague theoretical concepts which can dramatically boost the learning capability of students in different fields such as microbiology, computer architecture, biomedical research, automobile engineering, and structural engineering.

Use Cases of AR and VR in Learning

AR and VR poised to disrupt our traditional learning environment and can change the way how we learn and teach. It has plethora of use cases in the learning field and some of the most prominent are as follows:

  1. Student Participation in the Classroom

Implementing AR and VR in the classroom convince students to actively participate in the learning field by making their conventional learning environment highly interactive and engaging. To keep that in perspective, let’s say in the zoology lecture students are learning about the different varieties of birds. With AR-driven app, they can perceive all of these in the real-world environment.

  • Student Recruitment

Virtual tours enable students to enjoy the dilemma of visiting the school or university physically. Though these virtual tours are not cost effective right now. VR allows students to see how well-equipped labs and classroom of their intended universities are and thereby they can make a better decisions while selecting university for the higher education.

  • Medical Education and Training

With the help of immersive technologies, teachers can better explain their student theoretical concepts of Physics, Chemistry, and Biology. To keep that in perspective, let’s say teacher wants to explain sp3 hybridization model to his/her student, with AR and VR-enabled devices, he can better explain his/her students geometrical arrangement of an atoms in a molecule. Apart from that, these devices can help students to better visualize the neurological functions within the human brain. Let’s say, a neuroscientist is studying how sleep deficiency can affect cognitive behavior. Herein the power of these immersive technologies come into play. These devices positively effectuate the neurological studies.

Besides, these immersive technologies allow the STEM students to grasp their understanding about both the theoretical and practical aspects of any subject. On the flip side, AR and VR-enabled headsets enabled the students to explore an innovative solutions of any given problem that are more appealing to the students. Apart from that, it also paves the way for safe and effective medical simulations to teach newbie medical practitioners and paramedics.

  • Classroom Education

It is quite evident from numerous studies that immersive technologies plays a decisive role in retaining information in students.  Apart from that, these technologies enabled students to glean valuable insights of any subject they were being taught.  For instance, students are learning the history of dinosaurs in their classroom now with the help of these devices, they can perceive dinosaurs in the real-world environment.

Conclusion

AR and VR-enabled apps and devices have very promising future growth and they are likely to shape our learning behavior. The realm of possibilities and the potential application of AR and VR in learning field is growing exponentially. Since our world is heading towards the AR and VR-enables learning experienced, there has never been a great time than now to immerse yourself into this technology.

Future of virtual reality gaming

VR gaming is the expression used to designate a modern age of VR development video games that offers gamers a fully interactive, last ever league viewpoint. Via a range of Video games equipment and facilities, like VR headphones, sensing gloves, hand controllers, and also more, players can observe and manipulate the virtual space.

On integrated units, advanced gaming systems, and then using modified computers and Chromebooks which can support prevailing Virtual reality headsets like Oculus Rift, HTC Vive, and Lenovo Mirage Solo, VR games are played.

Virtual reality (VR) is not a domain at any moment. VR technologies are developing rapidly, reaching many areas of a business. VR is transforming the way the system works, from medicine to the automobile industry, as dreams and aspirations stand intact. That’s also essential for video games especially.

Video games’ target audience started to change and develop, though one thing stays as much; players are trying to find the best overall multiplayer experience. As VR continues its move into another videogame industry, players will look to try this new kind of game, and then want to do that at a reasonable price.

Even amongst the gaming world, VR slot machines have become much more popular. They make it possible for gamers to fully understand VR gaming despite needing to purchase their special headphones. According to Super Data Research, the VR gaming industry as a service earned $286.7 million last year but could grow the industry by $2.3 billion by 2020. While VR arcade games are not widely distributed, they serve a focus and population increase.

You apparently wouldn’t think about activities that encourage education when you think about virtual reality. How you may not know is that certain forward-looking corporations with learning environment are earning high.

Much as professors have numerous teaching strategies, children have good methods of learning. Teachers also found that immersive games can be a very great way for students to learn the content in the classrooms.

Firms such as z Space create “Virtual Optimized feature” driven by software for equipment and VR classroom applications. These new developments keep students engaged and interested, as well as creating amazing new kinds of teacher content for lessons. There are multiple universes, restricted by just the human imagination of game developers. As demonstrated by fast investing currently taking place in the real world, the academic development (EdTech) sector has been well informed about what VR could add to a learning mix.

There are always a large lot of exceptions that VR gaming designers have to address, but progress will proceed as visibility and interest rise. For years, the gaming industry is now shifting and it has accomplished well at a progressive pace. VR technology does not see the quick progress they initially planned, before we realize it the best is yet to come and would be more popular.

Although the E3 convention remains the same because of either a pitch in the broad immersive players, the production of cheap touch pictures is growing. With big game developers such as Bethesda now focused on large VR tasks, players begin to have clear motivations for utilizing a tool.

Since then, emerging companies have been working to build less florid, frustrating, and more functional VR equipment.

The latest Vive and the Oculus goggles will require great game systems that can be powered so that more PC enthusiasts can’t use them. Sony’s PSVR is however compatible with the quick to locate PS4 console, which greatly reduces the overall cost of the device and means that games console users are comfortable with the core features of the framework.

Will VR ever fully dominate the gaming world through obsolete standard PC and console games? This is questionable. That is questionable. Many games, including Oculus Rift’s Lucky’s Novel, have indeed emerged out on Wearable technologies and players have been blamed because they have not made the most out of every functionality.

It is the case that VR gaming remains beside each other with conventional gaming and just takes on the titles that are better adapted to interactive games.

Although the eventual launches of residential VR have declined to live up to the expectations, rivalry among channels and game designers might progress to the VR headset being a frequent feature in the inventory of all but the most pc gamer.

Some other fascinating idea would be that, relative to much more costly, completely interactive design, residential VR systems would not attract the attention of the people. This can result in most games at home keeping to traditional platforms and enjoying their VR fix in arcade machines and entertainment venues that provides greater specifications and also more persuasive isolation.

Receipt recognition with Azure

We are constantly looking for ways to help you get the most out of your data. Our customer ask us a POC to recognize information from receipts.

Expense reports can be a very cumbersome and time-consuming task. Between all the manual data entry, approval workflows, and auditing, there are many pain points across the end-to-end process. With the you can minimize those pain points and increase the productivity of your employees, delivering real value back to your business.

Receipt processing lets you read and save key information from common sales receipts, like those used in restaurants, gas stations, retail, and more. Using this information, you can automatically pre-populate expense reports simply by scanning photos of your receipts. And when you automate the process at a large scale, there is the potential to save you and your business valuable time and money.

The prebuilt model uses state-of-the-art optical character recognition (OCR) to extract both printed and handwritten text from receipts. You can retrieve valuable information such as the merchant details, transaction date and time, list of purchased items, tax, and totals.

No training or prior configuration is required to use this prebuilt model. Start processing receipts right away in your apps and flows using the new canvas app component and AI Builder flow action.

Text translation

You can now use AI Builder to easily translate text to more than 60 languages. This prebuilt model is powered by the latest innovations in machine translation. You can use Text translation to process text in real-time from different languages from your customers worldwide, for internal and external communications and to keep language consistency in the text data that you store. Now available in preview, no trial or subscription required to try this feature.

Building a Text Classifier using Azure Machine Learning

Recently a client came to us to see if we could help them automate their RFP distribution system.  Currently the client has an employee manually check several websites for RFPs and alert the appropriate business vertical when a relevant RFP is found.  The current system requires manual data scraping, meaning the process is slow and results in RFPs being missed.  For the proof of concept phase with the client, we decided to build a machine learning model to classify the RFPs correctly and provide a way to automate the routing of the RFPs.  The client wanted to break the project into stages so once the initial Proof of Concept was successful, other parts required to automate the whole process would receive the go-ahead. If you would like a proof of concept, visit our Business Analytics page for information.

Due to the abbreviated time-period, we decided to use Microsoft’s Azure Machine Learning Studio to build the model.  Azure Machine Learning Studio provided great visualizations of the model for the client.  When developing an end-to-end solution for the client, Azure Machine Learning Service will be implemented.  If you are curious about the differences between Azure ML Studio and Azure ML Service, this article provides an excellent explanation. 

Pre-Work 

First I looked through the Azure AI Gallery to see if there were any projects that would provide guidance in building our text classifier. I found the BBC News Classifier was a great fit. 

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Model Evaluation – Confusion Matrix:  

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If the model is built correctly, one should see distribution like what is shown above.  The model assigns a probability per category to reflect its confidence in how to categorize each story.  It is normal for a news story to be classified in one main class, but the model recognizes there is a probability that the story could belong to multiple classes. 

The metrics from the model also showed good accuracy on the model. 

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Step 1: Receiving and cleaning the data. 

The client uploaded several RFPs into different folders in Teams that were labeled with the client’s verticals.  One of the challenges not solved in this POC is scraping the data from an RFP.  Our focus was on starting small with the classifier to keep things moving forward. Every municipality creates their own version of an RFP, so most RFPs are not uniform.  For this POC, the RFP summary data was scraped manually and added to a data file. 

Step 2: Create the Model 

To start, we followed the BBC News Classifier model outline.  The R-Script module and the text_processing.zip found in the BBC News Classifier were switched with the pre-built Preprocess Text module.  When the initial model was run, it classified all the data into the one bucket that had the largest number of examples.  The model was run again including only data with labels with a high number of examples and a comparable amount in each bucket.  Again, poor results.  Time to re-think the model. 

Microsoft has a great reference library around the modules available in Machine Learning Studio.  While looking through the documentation around Text Analytics, two modules additional modules were found to test: “Extract Key Phrases from Text” and “Extract N-Gram Features from Text.”  The Extracting Key Phrases from Text module extracts one or more phrases deemed meaningful.  The Extract N-Gram Features from Text module creates a dictionary of n-grams from free text and identifies the n-grams that have the most information value.  The new model was run with a Multi-class Decision Forest algorithm instead of the Multi-Class Neural Network.  When the model was run with all the category labels, the results were closer to what was expected, but not yielding accurate results. 

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One drawback was the labels with minimal data were not classifying correctly.  The model was re-run with only category labels with higher and comparable amounts of data. 

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Whoops! That was a step in the wrong direction.  Maybe the n-gram feature wasn’t the best text analytics module to try.  What happens if Feature Hashing is used instead?  Feature Hashing transforms a stream of English text into a set of features represented as integers.  The hashed features can then be passed to the machine learning algorithm to train the text analysis model. 

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Well, that accuracy is much better but maybe a bit too good.  Even though the lowest number of decision trees, least amount of depth, and the least number of random splits were used the accuracy of the model was too good. We should expect to see some distribution or a small probability that the RFP could be classified in other categories. 

This could be due to the size of the dataset that is being used.  It was good to find out that Feature Hashing does a better job than Extracting Key Phrases from Text or Extracting N-Gram Features.  What happens if a different machine learning algorithm (like the Multi-Class Neural Network) is used? 

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This is the best model yet.  Distribution is across category labels as expected.  There is a good chance of overfitting, but that can be worked out with additional data added to the model. 

Since this was the best model yet, it was re-run will data from all category labels. 

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Results were encouraging, but clearly more data will be required to appropriately label all categories.  As more data is added, there will be more improvements to the model.  Two options worth considering would be applying an ensemble approach or trying NLP techniques like entity extraction, chunking, or isolating nouns and verbs. 

Step 3:  Automate the Model 

Azure Machine Learning Studio’s option to Set Up a Web Service was used to create a Predictive Experiment and deploy as a web service.  Then using the ML Studio add-in in Excel, a template was created where data can be added, the model can be run, and predictions bucketed into a scored probability column. 

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The next step was to create a table that reads the predicted data that can be picked up by a Flow.  The Flow is set up to send a notification to a channel on Microsoft Teams. 

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This is not a final solution.  Several additional steps in a further POC will be needed to be completed to set up a fully automated solution, but the initial results are promising.  What’s important to understand is how flexible this process can be. If the client scoped a different set of requirements, or was in a different industry, we could easily tailor a solution to fit their pain. 

How AI Is used in the Algorithmic Trading Sector

Artificial intelligence (AI) is rapidly transforming the global financial services industry, playing a key role in everything from fraud detection and compliance to banking chatbots and robo-advisory services. It’s also changing the ever-evolving world of algorithmic trading helping to eliminate human error and streamlining decision-making processes. But, how exactly is AI utilised in this sector and what are the overall benefits? Let’s take a closer look.

What exactly is AI?

Before you can really get to grips with how AI is used in the algorithmic trading sector, you must first understand what it is. Coined in 1955 by John McCarthy, AI is a term which describes the intelligence displayed by machines, in contrast to the natural intelligence displayed by humans. AI systems will typically demonstrate at least some of the following behaviors including planning, learning, reasoning, problem-solving, knowledge representation and perception.

Important AI applications

While AI is rather broad by definition, there are specific branches that play a prominent role within the algorithmic trading sector including ‘machine learning’ (ML). Named by Arthur Samuel of IBM in 1959, ML is an AI application that focusses on the idea that machines can learn for themselves by accessing Big Data. Such systems can automatically improve based on experience, without being explicitly programmed.

‘Deep learning’ (DL) is another AI concept and a branch of ML which revolved around problem-solving. Such networks do not necessarily need structure or labels to make sense of data. You may have also across ‘neural networks.’ These have AI roots and are inspired by the way humans think. They’re becoming increasingly integrated into today’s AI-related trading world.

Algorithmic trading uses powerful computers, running complex mathematical formulas, to generate returns. This is very different from days gone by where humans used to crowd busy exchanges or pick out the best assets to buy and sell from an office.

Sophisticated algorithms now play a significant role in market transactions and while algorithmic trading isn’t necessarily new, artificial intelligence is giving algorithmic traders extra tools to enhance their performance. Indeed, feeding AI predictions into algorithms can give you a more solid overview of the market including when to enter and exit positions and the best assets to long and short.

So, how exactly does AI tie in with today’s algorithmic trading sector?

Well, algorithmic trading is all about executing orders using automated and pre-programmed trading instructions, accounting for numerous variables such as volume, price and time. Algorithmic trading nowadays involves the use of complex AI systems with computers generating 50-70% of equity market trades, 60% of futures trades and 50% of treasuries. The benefits of AI in algorithmic trading.

Fast trading speeds and improved accuracy

When it comes to algorithmic trading, large numbers of orders are executed within seconds adding liquidity to the market. High-Frequency Trading (HFT) of this kind happens in a fraction of a second and simply can’t be done by humans alone – that’s why algorithms are needed to execute and place bids before the market changes.

Automation streamlines the entire process with AI and machine learning adding an extra clever twist. Essentially ML computer systems are trained to recognise market movements with impressive accuracy, helping algorithms to bid accordingly. By accessing and understanding large data sets, ML systems can predict future outcomes, enhance trading strategies and tweak portfolios accordingly.

AI-enhanced algorithmic trading therefore helps to improve the performance and meet the demands of target clientele including hedge funds, propriety trading houses, corporates, bank propriety trading desks and next-generation marketing makers.

Elimination of human error

Algorithmic trading also helps to reduce errors based on emotional and psychological factors. Often, traders let past trades, FOMO or market pressures affect their judgement and this can lead to poor decision making.

But with algorithmic trading, algorithms are used to ensure trader order placement is instance and accurate – based on pre-defined sets of instructions.

With the help of AI, it’s also possible for computer systems to check multiple market conditions and adjust trades instantly depending on the market environment. Of course, if this were to be done manually, it would take hours and hours of physical labour, research and fact-checking. And even then, errors might occur. Opportunities are likely to be missed too which is why AI is rapidly being integrated into financial institutions and shaping the sector significantly.

AI and algorithmic trading in the real world

AI is not just something that’s being talked about. It’s already here and changing the financial world significantly, especially when it comes to trading practices. Top financial institutions including UBS and JP Morgan have already introduced AI into their trading tools with the former using AI techniques to trade volatility (which is notoriously difficult to navigate) and the latter using AI algorithms to execute equity trades. Algorithms enhanced by AI are also being used to guide venture capitalist investments.

So, as you can see, AI is being increasingly utilised in the algorithmic trading sector and offers many benefits. As 80% of all data is completely unstructured, AI and its complexed applications including ML and DL aims to deliver a more structured, organised and data-fuelled approach to the trading world, helping to make the whole process efficient, while providing split-second insights.

Overview Of Azure Kinect

Pre-requisite Knowledge

 Before we start with the understanding of what is Azure Kinect, we should know,

Background I would like to explain the short information about ‘Artificial Intelligence and Kinect’ before jumping in to ‘Azure Kinect’. 

What is Artificial Intelligence

 In simple words ‘Artificial Intelligence (AI)’ is the artificial creation of the system like a human who can observe, react, learn, plan and process the instructions, virtual reality and provide intelligence on it. It is rapidly emerging technology and internet enable technology. Sometimes AI is also called as Machine Learning. 

What is Kinect and its background

 Kinect is the motion sensor device using in Xbox 360 gaming console. This device provides natural user interface to interact with it without any intermediate device. This device has capability of face detection as well as the voice recognition. This device has 3D camera which creates the virtual images and with the help of motion sensor it detects the movements of the images. The first-generation Kinect for Xbox 360 was introduced in November 2010. This device was originally created for gaming purpose, but now a days this technology is applying to real worlds applications in the virtual shopping, education, healthcare industries, digital signage etc. This product is developed by Microsoft. 

Introduction of Azure Kinect

 As I explained above Kinect is the motion sensor device. Azure Kinect device has,

  1. DK camera system
  2. 1MP depth camera
  3. 360-degree microphone
  4. 12MP RGB camera
  5. Orientation senor
  6. Size and weight – 103 x 39 x 126 mm and weighs only 440g
Overview Of Azure Kinect

Image Source – Microsoft Docs Azure Kinect has ability to create platform for developers with Artificial tools and plug this in to the Azure cloud for cloud-based service, computer vision and speech models. Azure Kinect has its own developer kit (DK) by Microsoft which is available in the portal site here. Microsoft Azure Kinect SDK has new sensor SDK, body tracking SDK, vision APIs, speech service SDK for Azure Kinect DK. This is the latest released feature by Microsoft for Azure cloud. Please note that Azure Kinect DK is not designed for use with Xbox. By using Azure Kinect, now we can build the applications like cashier less stores, manage inventory of the products, track the patient movements integrate these motions with the AI in hospital, enhance physical therapy, improve and monitor athletic performance, computer vision and speech models etc. We can enhance feature of Azure Kinect application with Azure cognitive services. Transcribe and translate speech in real time using Speech Services. Add object, scene, and activity detection or optical character recognition using Computer Vision or use Azure IoT Edge to manage PCs connected to your Azure Kinect DK device. 

Overview Of Azure Kinect

Image Source – Microsoft Docs Azure Kinect device price is $399.00 and can be purchased from Microsoft’s store here. As of now (12th August 2019) this product is only available in the US and China. 

Inside of Azure Kinect DK

Overview Of Azure Kinect

 Image Source – Microsoft Docs

  1. 1MP depth sensor with FOV option
  2. 7-microphone array for speech and sound capture
  3. 12-MP RGB video camera for an additional color stream
  4. Accelerometer and gyroscope (IMU) for sensor orientation and spatial tracking
  5. External sync pins to easily synchronize sensor streams from multiple Kinect devices
  6. Azure Kinect Developer Kit
  7. Purchase Azure Kinect from Microsoft Store

7 Benefits of Artificial Intelligence for Business

Artificial Intelligence can have a very positive impact on a business. Here’s how…

One of the newest benefits of cloud computing is that it enables businesses to take advantage of artificial intelligence (AI). This rapidly developing technology offers significant development opportunities that many companies have already been quick to seize upon. In this post, we’ll look at some of the ways your company can benefit from cloud-based AI.

1. Improving personalised shopping experiences

Providing customers with personalised marketing increases engagement, helps generate customer loyalty and improves sales. This is why companies are putting so much effort into it. One of the advantages of using AI is that it is able to identify patterns in customers’ browsing habits and purchasing behaviour. Using the millions of transactions stored and analysed in the cloud, AI is able to provide highly accurate offers to individual customers.

2. Automating customer interactions

Most customer interactions, such as emails, online chat, social media conversations and telephone calls, currently require human involvement. AI, however, is enabling companies to automate these communications. By analysing data collected from previous communications it is possible to program computers to respond accurately to customers and deal with their enquiries. What’s more, when AI is combined with machine learning, the more the AI platforms interact, the better they become.

One example of this is AI Chatbots which, unlike humans, can interact with unlimited customers at the same time and can both respond and initiate communication – whether on a website or an app.

It is estimated, that by 2020, 85 percent of all customer interactions will be taken care of by intelligent machines that are able to replicate human functions. The days of using a call centre look like they are coming to a close.

3. Real-time Assistance

AI is also useful for businesses that need to constantly communicate with high volumes of customers throughout each day. For example, in the transport industry, bus, train and airlines companies, which can have millions of passengers a day, can use AI to interact, in real-time, to send personalised travel information, such as notice of delays. Some bus companies, for example, are already tracking the location of their buses and using AI to provide travellers with real-time updates about where the bus is along its route and its estimated time of arrival. Customers receive this information on the bus company’s app.

4. Data mining

One of the biggest advantages of using cloud-based AI is that artificial intelligence apps are able to quickly discover important and relevant findings during the processing of big data. This can provide businesses with previously undiscovered insights that can help give it an advantage in the marketplace.

5.Operational automation

AI is able to operate other technologies that increase automation in business. For example, AI can be used to control robots in factories or maintain ideal temperatures through intelligent heating. In Japan, human-looking robots now serve as receptionists in some of the countries’ hotels automating check-ins, booking services and dealing (in four languages) with customer enquiries. In retail, AI is also being linked with RFID and cloud technology to track inventory. In China, police forces use AI to catch criminals. The country has a vast CCTV network and AI uses facial recognition to spot and track suspects so that they can be apprehended.

6.Predicting outcomes

Another advantage of AI is that it is able to predict outcomes based on data analysis. For example, it sees patterns in customer data that can show whether the products currently on sale are likely to sell and in what volumes. It will also predict when the demand will tail off. This can be very useful in helping a company purchase the correct stock and in the correct volumes. It is predicted that, within 10 years, the days of seasonal sales will be over as AI will mean there is too little leftover stock to sell off.

This ability to predict is not just useful in retail. AI is also being used in many other areas, for example, in banking where it can predict currency and stock price fluctuations or in healthcare where, remarkably, it can predict outbreaks of infections by analysing social media posts.

7.Improve the recruitment process

It may be bad news for recruitment companies, but AI is now helping businesses automate the recruitment of new employees. It is able to quickly sift through applications, automatically rejecting those which do not meet the company’s personal specification. This not only saves time (or money spent on a recruitment agency), but it also ensures that there is no discrimination or bias in the shortlisting process. The AI programs available can even take care of the many administrative tasks of recruitment.

Conclusion

As you can see, AI systems provide businesses with a wide range of benefits, including personalised marketing, customer service, operational automation, inventory management and recruitment. And these are just a few of the many ways AI can be used. What’s remarkable, however, is that many of the AI apps, which are designed specifically for cloud-based systems, are quickly and easily deployable. Companies whose systems are in the cloud can be benefitting from them in no time at all.