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Artificial Intelligence

Using AI to make roller coasters safer

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Customer: One of the leading integrated resorts

Summary

The customer an integrated resort on the island in Singapore. They offer some world-class attractions one of which is the Battlestar Galactica, the most enticing duel rollercoaster ride at the resort. They decided to cater to preventive maintenance of the wheels of this ride to ensure top-class safety. They planned to adopt Machine Learning (ML) based solution via Google cloud platform (GCP).

Problem Statement

  • The Customer’s Battlestar Galactica ride is financially quite demanding and requires high maintenance.
  • The wheel detection process is time-consuming and a high maintenance manual job.
  • Decision making on the good versus the bad wheel is based on human judgement and expert’s experience.

The ultimate goal was to remove human intervention and automate the decision making on the identification of a bad wheel using machine learning. The machine learning model needed to be trained on currently available data and ingest real-time data over a period of time to help identify patterns of range and intensity values of wheels. This would in turn help in identifying the wheel as good or bad at the end of every run.

Proposed Solution

Project pre-requisites

Ordering of .dat files generated by SICK cameras to be maintained in a single date-time format for appropriate Radio-frequency identification (RFID) wheel mapping. Bad wheel data should be stored in the same format as a good wheel (.dat files) in order to train the classifier. The dashboard to contain the trend of intensity and height values. Single folder to be maintained for Cam_01 and another folder for Cam_02, folder name or location should not change.

Solution

  • Data ingestion and storage

An image capturing software tool named Ranger Studio was used to absorb the complete information on wheels. The Ranger Studio onsite machine generates .dat files for wheels post every run and stores in a local machine. An upload service picks these .dat files from the storage location at pre-defined intervals and runs C# code on it to provide CSV output with range and intensity values.

CSV files are pushed to Google Cloud Services (GCS) using Google Pub/Sub real-time messaging service. The publisher is used to publish files from the local machine using two separate python scripts for Cam01 and Cam02. The subscriber is then used to subscribe to the published files for Cam01 and Cam02.

  • Data Processing

Powerup is responsible to ingest the data into cloud storage or cloud SQL based on the defined format. Processing of data would include the timestamp and wheel run count. There is a pub tracker and a sub tracker maintained to track the files for both cameras so that the subscribed files can be stored on GCS for both the cameras separately. After CSV data is processed, it is removed from the local machine via a custom script to avoid memory issues.

  • Data modelling Cloud SQL

Once data is processed, Powerup to design the data model in cloud SQL where all the data points will be stored in relational format.

The CSV files of individual wheels are then used to train the classifier model. The classifier model is built with an application programming interface named Keras. The trained classifier helps generate a prediction model (.pkl file) to identify good and bad wheels. The prediction model resides on a GCP VM. The generated CSV files are passed through the prediction model and are classified as good or bad based on an accuracy value.

  • Big Query and ML Model

Once the prediction for a wheel is done, the predicted accuracy score, timestamp and wheel information is stored into the Big Query tables. The average wheel accuracy for wheels is then displayed on Google Data Studio.

Powerup to ensure optimization of data performance via tuning and build the ML model. This would enable the customer to obtain large volumes of height and intensity data, post which, they score the ML model with new data.

Current accuracy threshold for SMS trigger is set at 70. Accuracy of prediction is set to improve over a period 6 months when the model has enough bad wheel data reported for training the ML classifier model. SMS will be triggered if the accuracy value is below 70.

SMS will also be triggered if a file is not received from the local machine to Google Cloud Storage via Google Pub/Sub. The reason for file not being received needs to be checked by the client’s SICK team as it may be due to multiple reasons like source file not generated due to camera malfunction, system shutdown or maintenance and so on. Powerup team to be informed about the same as the restart of instances may be required in such cases. Twilio is the service used for SMS whereas SendGrid is used for email notifications.

  • Security and Deployment

Powerup to build a secure environment for all third party integrations. Deploy User Acceptance Test (UAT) environments, conduct regression tests and provide

Go Live Support to off-site applications. The number of servers and services supported with the production was 10 where support included server management in terms of security, network, DevOps, backup DR and audit. Support also included adjusting ML models to improvise training.

 

Limitations

Since the request payload size was higher, Google ML / Online Predictor could not be used. A custom prediction model was built with Keras to overcome this.

Artificial Intelligence

Cloud platform

Google Cloud Platform.

Technologies used

Cloud Storage, Bog Query, Data Studio, Compute Engine.

Business Benefits

Powerup has successfully been able to train the classifier model with a limited set of good and bad wheel real-time data. The accuracy of the model is expected to improve over time. With current data, the accuracy of the model stands at 60% ensuring cost-effectiveness and world-class safety.

The Role of Artificial Intelligence in Building a Better World

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Written by Jeremiah Peter, Solution specialist-Advanced Services Group, Powerupcloud technologies

A Not So Distant Future

As we usher into an era dominated by technological innovation, Artificial Intelligence continues to draw heated debates for its unparalleled ability to automate tasks and eliminate human dependency. The growing cynicism on automation has captured our imagination in leading cinematic marvels such as ‘2001 A Space Odyssey’ and ‘Terminator’. Painting a deeply poignant future, these movies induce fears of a machine-led holocaust sparked by AI’s transcendence into Singularity- a point where AI supersedes human intelligence. Veering away from the dystopian narrative and objectively analyzing the realm of AI, it seems apparent that we can leverage AI for social good without descending into chaos. The call for beneficence in intelligent design is best captured by American computer scientist, Alan Kay’s words- “The best way to predict the future is to invent it”.

The blog presents a new frame of analysis for the responsible use of Artificial Intelligence technologies to augment human and social development. Additionally, the blog also delineates key Machine Learning and Computer Vision (Object Detection) concepts to solve a real-world problem, outlining a discourse for pragmatic solutions with broad social impact.

The Next Frontier

Under the dense canopy of commercial AI clutter, there are several AI initiatives that continue to garner both awe and adulation in the social sciences and humanities spectrum. Cancer detection algorithms, disaster forecast systems and voice-enabled navigation for the visually impaired are a few notable mentions. Although socially-relevant applications have achieved a fair degree of implementational success, they fail to attain outreach at a global level due to a lack of data accessibility and the dearth of AI talent.

Alternatively, innovative technologies that supplement large scale human-assistance programs in enhancing efficacy could be considered a worthwhile undertaking. Infusing computer vision technology in human-centered programs can dramatically improve last-mile coverage, enhance transparency, mitigate risks and measure the overall impact of assistance programs. In the next section, we delve into some core issues that afflict large-scale human assistance programs and underscore the need for technological intervention.

The State of Human-assistance Programs

According to The State of Food Security and Nutrition in the World Report (2019), around 820 million people in the world are hungry and over 66 million of them are under the age of 5. With numbers increasing steeply in most parts of Africa, South America, and Asia, the fate of Sustainable Development Goal of Zero Hunger by 2030 hangs by a thread. Perturbed by the growing scourge, some nations responded by releasing a slew of measures to take corrective action.

One such initiative called The Midday Meal Scheme (MDMS), launched by the government of India in 1995, serves around 120 million children across government and government-aided schools. Recognized as one of the largest food assistance programs in the world, MDMS was laid out with a bold vision to enhance enrolment, retention, and attendance with the overarching aim of improving nutritional status among children in India. However, not including the logistical and infrastructural shortcomings, the initiative loses significant funds to pilferage each year (Source: CAG Report2015). Shackled by a lack of resources, the program struggles to counter aberrant practices with institutional measures and seeks remediation through innovative solutions.

Given the unprecedented magnitude, large-scale human assistance schemes such as MDMS require well-crafted solutions that can instill governance and accountability into their dispensation process. In our constant endeavor to design Intelligent Apps with profound social impact, Powerup experts examined a few libraries and models to carve out a computer vision model that could reign in governance under such programs. The following section explores the pre-requisites for formulating a desirable solution.

Empowering Social Initiatives with Object Detection

Evidently, the success of an AI application is hugely predicated on a well-conceived algorithm that can handle varying degrees of complexity. However, developing a nuanced program from scratch is often a cumbersome and time-intensive process. To accelerate application development, programmers usually rely on pre-compiled libraries, which are frequently accessed code routines used iteratively in the program. After gleaning several open-source image processing libraries (VXL, AForge.Net, LTI-Lib), Powerup team narrowed down on OpenCV for its unique image processing functions and algorithms.

Besides a good library, the solution also requires a robust image classification engine to parse objects and scenes within images. However, despite several key advances in vision technology, most classification systems fail to interact with the complexities of the physical world in that these systems can only identify a limited set of objects under a controlled environment. To develop advanced object detection capabilities, the application needs to be powered by an intelligent model that can elicit a more refined outcome- to make sense of what it sees.

In order to develop a broad understanding of the real-world, computer vision systems require comprehensive datasets that consist of a vast array of labeled images to facilitate object detection within acceptable bounds of accuracy. Apart from identifying the position of the object/s in the image, the engine should also be able to discern the relationship between objects and stitch a coherent story. Imagenet is a diverse open-source dataset that has over a billion labeled images and, perhaps, serves as a foundation for similar computer vision explorations.

Moreover, computer vision systems also hinge on neural networks for developing self-learning capabilities. Though popular deep-learning based models such as R-CNN, R-FCN, and SSD offer ground-breaking features, YOLO (You Only Look Once) stands out for its capabilities in super real-time object detection, clocking an impressive 45 FPS on GPU. The high processing power enables the application to not only interact with images but also process videos in real-time. Apart from an impressive processing capacity, the YOLO9000 is trained on both the ImageNet classification dataset and the COCO detection dataset that enables the model to interact with a diverse set of object classes. We labeled and annotated local food image sets containing items such as rice, eggs, beans, etc. to sensitize the model toward domain specific data.

As demonstrated in the image above, the model employs bounding boxes to identify individuals and food items in the picture. Acting as a robust deterrent against pilferage, the application can help induce more accountability, better scalability, and improved governance.

A New Reckoning

While a seventh of the population goes hungry every day, a third of the food in the world is wasted. The figure serves as a cause for deep contemplation of the growing disparities in a world spawned by industrialization and capitalism. As we stand on the cusp of modern society, phenomena such as unbridled population growth, disease control, climate change and unequal distribution of resources continue to present grave new challenges. Seeking innovative and sustainable solutions, therefore, becomes not just a moral obligation, but also the 21st century imperative.

Aside from the broad benefits, the domain of AI also presents a few substantive concerns that need general oversight. To that effect, the evolving technological landscape presents two inherent risks: wilful misuse (Eg- Cambridge Analytica Case) and unintended consequences (COMPAS- a biased parole granting application).

While concerns such as wilful misuse raise moral questions pertaining to data governance and preservation of user self-determination, risks such as algorithm bias and inexplicability of decision-making expose design loopholes. However, these apprehensions can be largely mitigated through a commonly accepted framework that is vetted by civil society organizations, academe, tech & business community, and policymakers around the globe. Owing to this pressing need, the European Parliament launched AI4People in February 2018 to design a unified charter for intelligent AI-based design. Upholding values such as protection of human self-determination, privacy, and transparency, the initiative is aimed at proposing recommendations, subsequently leading to policies, for ethical and socially preferable development of AI.

Governed by ethical tenets, innovative solutions such as Object Detection can operate within the purview of the proposed framework to alleviate new-age challenges. With reasonable caution and radical solution-seeking, AI promises to be an indispensable vector of change that can transform society by amplifying human agency (what we can do).

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Chatbots 2.0 – The new Series of bots & their influence on Automation

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Written by Rishabh Sood, Associate Director — Advanced Services Group at Powerupcoud Technologies

Chatbots as a concept are not new. In fact, under the domain of Artificial Intelligence, the origin of chatbots is quite early, tracing back to as early as 1955. Alan Turing published “Complete Machinery & Intelligence”, starting an unending debate, “Can machines think?”, laying the foundation of the Turing test & eventually leading to ELIZA in 1966, the 1st ever chatbot. It failed to pass the Turing test but did start a horde of chatbots to follow, each one more mature than its predecessor.

The next few years saw a host of chatbots, from PARRY to ALICE, but hardly any saw the light of the day. The actual war on the chatbots started with the larger players coming into the picture. Apple led with Siri in 2010, followed closely by Google Now, Amazon’s Alexa & Microsoft’s Cortana. These chatbots made life a tad easier for the users, as they could now speak to Siri
to book an Uber or tell Alexa to switch off the lights (another way to make our lives more cushioned). While these chatbots did create a huge value to users in terms of making their daily chores automated (& speak to a companion, for the lonely ones), business still was a long way from extracting benefits from the automated conversational channel.

Fast track to the world of today & we see chatbots part of every business. Every company has budgets allocated for automating at least 1 process on chatbots. Oracle says that 80% of the businesses are already using or have plans to start using chatbots for major business functions by 2020.
Chatbots have been implemented across companies & functions, primarily with a focus on automating support systems (internal as well as external). Most of the bots available in the market today respond to user queries basis keywords/phrases match. The more advanced bots today use the concept of intent matching & entities extraction to respond to more complex user queries. A handful of bots today even interact with the enterprise
systems to provide real-time data to the users. Most of the commercially successful bots in the market today are text-based interactions.

Most of the bots in action today augment tasks, which are repeatable/predictable in nature. Such tasks, if not automated, would require considerable human effort, if not automated. These chatbots are powered by Natural Language Processing engines to identify the user’s intent (verb or action), which then is passed to the bot’s brain to execute a series of steps, to generate a response for the identified intent. A handful of bots also contain Natural Language Generation engines to generate conversations, with a human touch to it. Sadly, 99.9% of today’s implementations will still fail more than 60 years old Turing test.

It’s true that the conversational Engines, as chatbots are often referred to as, have been there for a couple of years, but the usefulness of their existence will now be brought to test. The last couple of months have seen a considerable improvement in how the conversational engines add value to the businesses, that someone refers to as the chatbot 2.0 wave.

At Powerup, we continuously spend efforts on researching & making our products & offerings better, to suit the increasing market demands. So, what can one expect from this new wave of bots? For starters, the whole world is moving towards voice-based interactions, the text remains only for the traditional few. So, the bots need to be equipped with the smart & intelligent voice to text engines, which can understand different accents & word pronunciations, in addition, to be able to extract the relevant text from the noise in the user’s query, to deliver actual value. The likes of Google & Microsoft have spent billions of dollars on voice to text engines, but the above still remains a tough nut to crack, keeping the accuracy of the voice-based system limited in the business world.

With the voice-based devices, such as Amazon Echo & Google Home, bring convenience & accessibility together. Being available for cheap & in mass (the smart speakers’ market is slated to grow to $11.79 billion by 2023), makes it a regular household item, rather than a luxury. The bots will have to start interacting with users via such devices, not limited to the
traditional channels of Web & Social. This will not only require the traditional voice to text layers to be built in, but specific skills (such as Alexa Voice Services for Alexa compatible devices) to be written. A key factor here is how the user experience on a platform that is purely voice-based (although Echo Spot also has a small screen attached to it), where visual rendering is almost nil, is seamless & equally engaging for the users, as is on traditional channels.

In 2017, 45% of the people globally were reported to have preferred speaking to a chatbot, rather than a human agent. 2 years down the line, chatbots are all set to become mainstream, rather than alternative sources of communication. But this poses a greater challenge for the companies into the business. The bots will now have to start delivering business value, in terms of ROI, conversions, conversation drops & metrics that matter to the business. HnM uses a bot that quizzes the users to understand their references & then show clothing recommendations basis the above-identified preferences. This significantly increased their conversion on customer queries.

The new age of chatbots has already started moving in a more conversational direction, rather than the rule-based response generation, which the earlier bots were capable of. This means the bots now understand human speech better & are able to sustain conversations with humans for longer periods. This has been possible due to the movement of the traditional intent & entity models on NLP to advancement on Neural networks & Convolutional networks, building word clouds & deriving relations on these to understand user queries.

Traditionally, Retail has remained the biggest adopter of the chatbots. According to Statista.com, Retail remained to occupy more than 50% of the chunk in the chatbots market till 2016. With the advancement being brought into the world of chatbots at lightning speed, other sectors are picking up the pace. Healthcare & Telecommunications, followed by Banking are joining the race of deriving business outputs via chatbots, reporting 27%, 25% & 20% acceptance in the area in 2018. The new wave of bots is slated to narrow this gap across sectors in terms of adoption further. A study released by Deloitte this year highlights the increase of internal chatbot use-cases growing more than customer-facing functions, reporting IT use-cases to be the highest.

Chatbots have always remained as a way of conversing with users. Businesses have always focused on how the experience on a chatbot can be improved for the end customer, while technology has focused on how chatbots can be made more intelligent. The bots, being one of the highest growing channels of communication with the customers, generates a host of data in the form of conversational logs. Businesses can derive a host of insights from this data,
as the adoption of bots among customers increases over the next couple of years. A challenge that most businesses will face would be the regulatory authorities, such as GDPR in the EU. How business work around these, would be interesting to see.

Mobile apps remain the widest adopted means of usage & communication in the 21 st century, but the customers are tired of installing multiple apps on their phones. An average user installs more than 50 apps on a smartphone, the trend is only going to change. With multiple players consolidating the usage of apps, users will limit the no of apps that get the coveted memory on their mobile phones. This will give an opportunity to the businesses to push chatbots as a communication channel, by integrating bots not only on their websites (mobile compatible of course) but other mobile adaptable channels, such as Google Assistant.

According to Harvard Business Review researchers, a 5-minute delay in responding to a customer query increases the chances of losing the customer by 100%, while a 10-minute delay increases this chance 4 times. This basic premise of customer service is taken care of by automated conversational engines, chatbots.

Chatbots have a bright future, especially with the technological advancement, availability & adaptability increasing. How the new age bots add value to the business, remains to be seen and monitored.

It would be great to hear what you think the future of automated user engagement would be and their degree of influence.