Stephen Collie Enterprises New Zealand

There are many opinions about how Artificial Intelligence (AI) is going to change the world with expectations about its capabilities for now and in the future. AI simply refers to intelligence displayed by machines in contrast to that displayed by humans. Although humans are intelligent, they cannot be programmed to exceed their current capabilities in the same way a machine can. This has led to the creation of smart machines that handle tasks otherwise difficult for humans to handle efficiently.

Artificial intelligence is gradually becoming a constant presence in many technological applications. From apps and websites that show accurate user recommendations to gaming predictions, it is changing user experience in many fields.

Fleet management is one of the areas that AI is disrupting. The growing need to put driver safety first without compromising cost or efficiency has led to the adoption of smart fleet management systems.

For the average driver, the presence of AI can be felt heavily in the use of smartphones and telematics devices that recommend the best routes to take in traffic. This used to be a herculean task marked by paper maps and listening to radio broadcasts of traffic routes; today, we have complex traffic apps that combine GPS and artificial intelligence to make drivers’ lives easier.

Fleets benefit from powerful AI-based applications that handle anything from route recommendation to road risk data analysis and even driver coaching. It provides the accuracy, efficiency, convenience, and ease that earlier technology failed to provide. As a result, it is becoming safer to transport goods and services.

What is AI Fleet Management?

AI fleet management is the use of artificial intelligence-based technology to manage fleet operations. In a constantly changing world, it streamlines the work of any fleet manager by gradually eliminating human error from the transport process.

AI-based recommendations ensure that fleet drivers, managers, and mechanics can make better decisions that improve the long-term performance of the fleet. It also serves as assistive technology, ensuring that drivers retain autonomy during each transport cycle. Here are some key aspects of fleet management that AI can optimize:

Real-time Fleet Analytics

Collecting data is a key element of any operational process because without analyzing past data, you cannot make informed decisions. With historical insights to inform millions of data points analyzed in real-time,  the result is the prioritization opportunities and risks so that fleet managers and drivers can determine the best course of action to take in potentially problematic situations.

AI fleet management systems can be used to collect data for predictive analytics; data such as traffic and road conditions, environmental hazards, real-time weather, and mechanical faults can be used to predict incoming risk. This allows fleet managers to make better routes, schedules, maintenance delivery, and dispatch arrangements that improve fleet outcomes and activities.

Finally, with AI-based analytics, drivers no longer need to go in blind and can stay prepared for any unexpected events.

Better Repair and Maintenance Decisions

In May 2019, autonomous driving car brand Tesla made headlines after debuting AI-based technology that allows Tesla vehicles to diagnose their faults accurately. Although this technology has existed for some time and has been seen in several modern cars, artificial intelligence is providing a more accurate self-diagnostics as well as solutions to faults.

AI ensures that potential faults can be predicted before they even happen. For example, a normal vehicle with a diagnostics system would most likely signal an engine problem when it has already occurred. On the other hand, AI-based Internet of Things (IoT), data analytics and predictive maintenance, can lead to fault detection long before it eventually happens. According to a study by McKinsey, predictive maintenance will reduce costs by 10-40%, downtime by 50% and capital investment by 3-5%.

Predictive maintenance gives managers and their mechanics more than enough time for repairs which could potentially prevent accidents. More importantly, AI can recommend the most efficient and cost-effective solutions to mechanical faults. This has two major benefits:

  • It saves mechanics’ time usually spent on diagnostics.
  • It gives managers a clearer picture of the state of their fleets at all times. This could mean that service managers could save a lot of routine maintenance costs by carrying out repairs only when the AI systems show potential faults.

Fleet Integration

One major problem with fleet operations, especially in large fleets, is the number of moving parts within the system that need to be accessed. Several departments need a continuous inflow of information that needs to be in sync with all other departmental operations. Although a skilled workforce can make this happen, it is time and labor-intensive.

An AI system could simplify the process by seamlessly integrating every department on a single platform and feeding them information simultaneously. Service managers can save time and costs on planning, maintenance and monitoring operations since all data on those operations are fully accessible. This ensures that all personnel across the different departments have access to the data that helps them make informed decisions. It also leads to a more cohesive fleet, since every department automatically works in sync with the others.

Simpler Recruitment Process

According to a report by the U.S. Bureau of Labor Statistics. The need for automotive and diesel technicians is expected to grow by up to 5% by 2028. The American Trucking Association estimates that there will be a shortfall of up to 175,000 truck drivers by 2026.

As the older generation drivers and technicians retire, there is a need for younger tech-savvy replacements; however, this presents a problem with onboarding and training. AI can simplify the onboarding process by capturing the specialized skills of these workers before they retire.

This is especially great for technicians with unique ways of carrying out their tasks. AI can also recommend the most qualified drivers that suit the needs of the company from a pool of thousands of applicants, reducing the strain on recruiters.

Fleet Management

Image Credits

How is AI Integrated with Fleet Management?

AI-integrated software is usually a sophisticated system made up of several devices and applications such as Internet of Things, predictive data analysis and machine learning systems, HD cameras and sensors, communication and display systems, and WiFi.

For example, AI-based fleet management platform Driveri, currently deployed in fleets across the country is a combination of all of these components. There are also many other AI-integrated fleet management systems with one or more of these components.

Before understanding how each of these parts combines to create a fleet management powerhouse, it is important to know what each one does.

Internet of Things (IoT)

The Internet of Things refers to a network of actuators and sensors continuously collecting data from their environment. In fleet management, IoT ensures that enough data is captured for analysis while promoting the seamless sharing of information between all stakeholders on the supply chain such as retailers and manufacturers.

IoT for fleet management works through the use of 3 main technologies:

  • Wireless Communication (4G, Bluetooth< WiFi ) convey relevant information
  • Global Positioning System (GPS) for accurate real-time location tracking
  • Onboard Diagnostics (such as OBDII and J1939) scanners for self-diagnostics and reporting

Machine Learning 

Machine learning technology allows fleets to learn from data collected over time and make managed adjustments based on that data. The result is the creation of smart systems in which AI can learn decision making capabilities that enable more effective handling of practical situations.

HD Cameras

Cameras ensure that video data can be captured, analyzed and accessed at any time leading to a better study of driver behavior, road conditions or hazards.

An AI system with all of the above components will be capable of performing the following tasks:

  • Collecting accurate road data and transmitting it to other devices
  • Passing information across every arm of the supply chain
  • Analyzing data in real-time and advising the driver on the best course of action
  • Detecting distracted or drowsy driving behaviors in drivers before they lead to accidents
  • Capturing full video footage of accidents from different external vehicle angles
  • Running Self-diagnostics and recommending solutions through predictive maintenance

This is significant because it creates a future of fleet management in which human error is reduced in different aspects of the transport cycle. This, in turn, could lead to better outcomes and cost savings.

How AI Fleet Management Will Shape the Future of Transportation

Today, the automotive vehicle industry is faced with several problems that affect fleet activities and profitability. If properly applied, AI can potentially solve these problems and create a better future for transportation.These problems include:

  • Resource prioritization and efficacy
  • Risky driving behaviors that lead to accidents
  • Road risks
  • Data collection and analysis
  • Cost containment
  • Compliance

Risky road behaviors such as distracted and drowsy driving are often accompanied by signs that drivers are told to look out for. These signs include:

  • Yawning
  • Constant blinking
  • Missing turns or exits
  • Drifting out of their lane
  • Slower reaction times
  • Picking up a cellphone

Ordinarily, managers rely on their drivers to avoid these signs and have no way of knowing if a driver had been texting while driving or nodding off at the wheel. Artificial intelligence systems could be trained to detect head turns, missed exits, yawning and blinking frequencies and other signs of risky behavior. These signals can be broadcast to fleet managers in real-time, allowing them to take corrective measures.

Changing road conditions present another challenge for managers because they are difficult to detect without proper technological tools. These conditions present a huge risk evident in the 42,000 deaths they cause annually. AI-based predictive technology can map reduce the risk associated with this problem by studying and mapping out routes while also drawing from data gathered by other vehicles. It can also be trained to make smart predictions about the weather and detect environmental changes such as fog before a driver reaches that point.

A good example of this type of risk assessment through data collection is Netradyne, whose product has already mapped out over 1 million unique miles of US roads. In the future, an extensive database of road conditions will be essential for promoting safety.

As discussed above, AI-based systems can help managers save costs through fuel economy and predictive maintenance. No matter what type of fleet you operate, from trucks to trains, city buses, or taxis, fuel and maintenance are major contributors to operational costs. Vehicles break down and fuel prices increase without warning, leading to more expenditure. The elimination of routine maintenance schedules using IoT self-diagnostics and fuel control could be the key to better cost containment in the future of fleet management.

Which is the Best Fleet Management Software?

Fortunately, AI-based fleet management software has gone from being dreamy concepts to reality. Several technology companies have created software that improves driver safety and fleet performance without compromising cost or efficiency.

In our research, we looked at the key components that made each one stand out.After analyzing their mapping capabilities, technological range, as well as sensor technologies, Driveri emerged as the best fleet management software due to the following features:

  • An artificial intelligence DriverAlert system that captures and analyze every minute of driving time.
  • Real-time analysis and feedback enabled by powerful Edge Computing capabilities.
  • Internal lens that detects drowsy or distracted driving behaviors such as yawning that alerts managers in real-time enabling quick action to mitigate risk.
  • Advanced data analysis system with more than 1 million unique miles of US roads analyzed and stored in an accessible database
  • Forward, side, and interior HD cameras that capture high-quality videos in real-time
  • Access to up to 100 hours of video playback for records and as evidence in the case of accidents in which there are legal consequences
  • 4G LTE / WiFi / BT connection within fleets, to send and receive data, view video and analyze risky behaviors
  • A mobile application for real-time feedback
  • Single module installation system for quick and easy installation

Final Thoughts

The future of transportation looks more promising than ever due to the exciting applications of AI in fleet management. Unpredictable road conditions, operational costs, and driver retention problems could easily become obsolete as fleets move to AI-based systems. Every stakeholder stands to benefit a lot from the efficiency and reliability of this technology because of a reduction in costs, accidents, driver turnover, and other problems which could reflect on the pricing of fleet services. It could also ensure that other road users remain safe.

This article was originally published on

Featured Image Credits: Pixabay

AI and Blockchain – Super Cool or a Little Creepy?

If you’re a tiny bit freaked out by the enormous potential of AI and blockchain, you’re not the only one. When Dolly the sheep was cloned in the 90s, a pertinent question arose. Just because we can, does it mean we should?

Just because AI and blockchain technologies combined may stop crimes before they happen, replace human jobs with robots, and assign every “thing” in the stratosphere an identity–does it mean they should?

Are AI and blockchain combined super cool or a little creepy? Let’s take a closer look.

Artificial Intelligence (AI)

AI and blockchain and are obviously two very different foundational technologies. AI uses machine learning to analyze complex data models, identify patterns, and recreate them, eventually mimicking the actions of a human being. Sophia the robot? Right. And yes… a little creepy.

Hanson Robotics may have treated the world to a dose of AI at its very best. But not everything that involves Artificial Intelligence is as visually stunning, unfathomably expensive (or mentally disturbing) as Sophia.

So, if you’re worried about robots wiping out the human race, you can probably relax for now. You have to admit, as creepy as the spontaneous jokes and flawless facial expressions were, they were also pretty cool:

AI isn’t all about making robots come to life either. In fact, we’re constantly using AI in daily applications, perhaps without even realizing. Think LinkedIn and its predictive text chat, Siri or Alexa, for slightly more subtle examples of AI without the paparazzi.


Without diving too deeply into the particularities of blockchain (you can read more about it here if you want to), its main characteristics are decentralization, transparency, immutability, and ability to govern autonomously with smart contracts.

Through blockchain technology, we can send funds from one country to another without worrying about conversion fees in next to no time. We can speculate on the future price of Bitcoin, track items in the supply chain, store public records securely, eradicate poverty, wipe out corruption, trade energy credits, give power back to musicians, and hundreds of other extremely cool things.

But blockchain has a few characteristics that can give the hibigeebies as well. Smart governance can be hard to get your head around, the idea that code is law, and that at some point in the future, it’s likely that IoT technology and blockchain will create autonomous things with no living person behind them.

“What happens when something goes bad? Who is responsible?” asked Malta’s Steve Tendon in his interview with us. “These questions need new laws to be addressed.”

When AI and Blockchain Work Together

There are several ways in which Artificial Intelligence and blockchain can work together, to help each other reach their full potential. For example, we’re still a long way off smart governance while issues exist with smart contracts. Namely that they’re only as good as the developer who programmed them and they cannot leave room for good faith or human interpretation. Perhaps through machine learning and AI, smart contracts can reach their full potential and autonomous societies can exist.

AI - Internet of Things

The decentralized nature of blockchain technology also means that AI computing power can be spread over nodes to drive down the cost and make it more accessible to all.

But, perhaps the main common thread between AI and blockchain is actually data. Sophia said in her speech, “think of me a smart input-output system.” Good AI is only as good as the data fed into it. This is where teaming the two technologies can get really useful (and really cool, as well).

Rather than an army of Sophias being poorly programmed and driverless cars crashing into pedestrians, blockchain can ensure that the data given machines is high-quality. And verified through its cryptography and immutability.

Since AI depends on meaningful data, blockchain could soon be a huge enabler to allow machines to learn faster and more efficiently. Not just Sophia, but financial transactions, smart contracts, predictive text, and more.

Some Everyday Examples

Intelligent Trading Foundation and AiX are examples of trading platforms that use AI and blockchain together. Making use of deep recurrent neural networks to make predictions on price movements of cryptocurrencies. By harnessing the latest AI tech, they can figure out whether the price of cryptocurrencies will go up or down. By using AI for intelligence and blockchain for trading, these platforms can provide the latest in technology on a secure platform.

DeepBrain Chain is an AI computing platform powered by blockchain technology to integrate computing resources that are currently scattered all over the world. Seeing a pattern? DATA.

DeepBrain Chain He Yong

In the energy industry, companies like Electrify are using blockchain to support AI and improve the efficiency of their network operations, reducing energy bills by as much as 25 percent. Blockchain can ensure the security and privacy of the data they process using AI and also provide enough data to make rigorous Artificial Intelligence possible.

In finance, AI and blockchain work together to improve efficiencies as well. Artificial Intelligence can automate the repetitive tasks and blockchain can secure the data. And the list of cool (and creepy) projects goes on.

The Takeaway

We may not have a Bladerunner situation on our hands or a Minority Report in which we find ourselves massacred by robots or slammed behind bars before committing a crime. But still, there is something a little ominous about a robot that looks like a human saying, “you be nice to me and I’ll be nice to you.”

This article was previously published on

About the Author:

Christina is a B2B writer and MBA, specializing in fintech, cybersecurity, blockchain, and other geeky areas. When she’s not at her computer, you’ll find her surfing, traveling, or relaxing with a glass of wine.


This article written by Fahad Munawar Khan and is originally posted at Toptal

Salesforce Einstein. The AI revolution is already transforming the consumer world.

Sometimes, it’s in everyday ways like product recommendations, and sometimes it’s in magnificent ways: Cochlear implants, which provide artificial hearing for those born completely deaf, have switched to AI for a superior end-user experience.

Artificial intelligence (AI) is the latest milestone in modern technology.

The AI revolution is leading to a smarter world, and this smarter world has been built on the mega-trends that we’ve all witnessed over the last 20 years: the web, the cloud, social, mobile, and the Internet of Things (IoT).

With cloud technology, we have, as developers, virtually unlimited computing and storage capacity, and it’s really that combination of massive data and massive computing power that’s leading to this revolution. Now that everybody is connected to each other and to everything in some way or another, all of these connections are generating orders of magnitude more data for the AI cloud to process than ever before.

You experience the AI cloud every day as a consumer. When you see a product recommendation on Amazon, a movie recommendation on Netflix, or a photo that’s automatically identified and tagged in your Facebook feed, you’re experiencing the power of AI.

Now wouldn’t it be great if the app you’re working on—whether it’s a Salesforce app or not—could somehow also provide these smarter, AI-powered experiences? For example, what if our business and our sales app could work together to tell us which leads are most likely to convert, or our service app could use the AI cloud to tell us which cases are likely to be escalated?

Unfortunately, for many teams, it seems too complex and expensive to apply AI to their app’s business process. First, it starts with data science and, to do data science, you have to collect and integrate all the required data. And then you need to do data wrangling, transforming the data so that you can use it for machine learning. And then, depending on your expertise, you may even need outside help from data scientists to build predictive models, maintain them, refresh them, and create an infrastructure that is trusted and secure and scalable. Then, after all that work, you have to take these predictions and put them into the context of the business user.

Enter Salesforce Einstein

Knowing that AI was often out of reach, Salesforce acquired companies like MetaMind (deep learning specialists), Implisit Insights (applying AI to the sales process specifically), and PredictionIO (machine learning and big data analytics) to help them build Salesforce Einstein.

Salesforce Einstein is AI for Salesforce, and it’s built right into the platform. As Einstein’s GM put it, “It takes the world’s number one CRM and makes it the world’s smartest CRM.” With Salesforce’s AI offering, you can now empower a company’s sales, service, marketing, and IT professionals to be their best by making every customer interaction faster, smarter, and more predictive.

Deep Learning: Einstein Vision and Einstein Language

Salesforce Einstein should bring the AI cloud within reach of developers. Maybe. But where should you start? The first thing to know is that AI has three major components:

  • Data
  • Algorithms
  • Computation

Big data in general has been a hot topic for the past couple of years. Everyone is excited to have new sources of data, new ways of analyzing it, and new ways of storing it.

This is going to be a big part of how we bring artificial intelligence into the enterprise but a lot of the AI development effort has been on the algorithm side. These are complex algorithms that are being built upon, expanded, and actually having new research from both the private and public sectors. You can be sure that AI cloud algorithms are going to continue to be innovative and continue to drive new features for your applications and for customer experiences.

The computation aspect you’ve probably heard of lately, too: GPUs, TPUs, new investments, and new research from all the best hardware companies are all going towards computing power, ensuring that these algorithms have the infrastructure they need to continue to be innovative and to be able to provide insight into your data.

Before we get into how you can develop something that leverages this technology, let’s dig into some more details of what you’ll be dealing with.

The Data

There are two forms of data to consider here: Structured and unstructured.

Structured and unstructured data.

Structured data includes your ERP data and the majority of your CRM data; it could be data coming out of IoT devices, for example. This type of data is already easily searchable by basic algorithms.

Unstructured data could be your image data, email messages, PowerPoint presentations, Word documents, etc. This is where deep learning and machine learning algorithms come in, vastly simplifying how we search through this type of data.

The Algorithm

Deep learning is a complicated term: A lot of developers and DevOps engineers get overwhelmed by it. They think they don’t have the infrastructure to handle neural networks. They think they need a PhD to sufficiently understand the state-of-the-art model to be incorporated in order to break it down into a more manageable thought process. Fortunately, knowing enough of it to be able to leverage it is a lot more accessible than that.

You only need to know about the input and output layers; all the really difficult work happens in the hidden layer, which is taken care of by the Salesforce AI cloud.

Let’s start this way: You have an input file. This could be an image, an audio file, or a text file. And you want to derive insightful output from it. Salesforce Einstein has a set of APIs which you can use to make this process really seamless: You don’t have to know anything about what happens in between.

With Salesforce Einstein, it is very easy for you to embed deep learning into your applications. It provides you with well-hosted infrastructure that manages your models as a service and thus takes care of any scalability needs. So it makes it easy for you to upload, train, and understand your model metrics, and in the end provide predictions in real time on a pre-trained model or a custom model that you create.

If even that sounds foreign to you, the next examples will quickly get you up to speed.

Computation: Einstein Vision

This is one of the deep learning services offered by Salesforce Einstein. Not all deep learning models or neural networks are equal: Specific architectures are used for specific problems. And in the case of computer vision, the AI cloud uses what is called a convolutional neural network, which means that each layer learns from the previous one. So when such a network is trained on image data, it rebuilds the image from the ground up to understand its different components. It will first look at an image’s smallest unit, the pixel, and then understand the edges, and then the next layer understands object parts or elements, and then eventually it gets to whole objects.

And that entire process is taken care of for you, so you can focus on the business value you can unlock for your clients with computer vision instead of on the process itself.

Einstein Vision can be trained to recognize objects in images.

Think about how the transportation industry could be transformed by using drones to monitor highways—no one would need to send a cleanup crew.

Or how CPG companies, instead of sending individuals to manually record the products on a shelf, could just take a picture and have it be automatically analyzed.

Or how consumer retail can be revolutionized with visual search, or how insurance companies can automate the triaging of claims, or how image processing can be leveraged by healthcare.

All such scenarios can be covered by Einstein Image Classification, which is part of Einstein Vision. All you need is a model, which is more or less just a set of classification labels.

Build a Custom Model

A custom model can predict the classification of new data based on a training dataset made of data with known classifications.

You can build your own custom models and then integrate these within your workflows, whether that’s a Salesforce workflow or an external application.

Building your own custom model involves just three steps:

  1. Create your own data set, based on what your custom model needs to do. Say you want to be able to tell the difference between three-door refrigerators and two-door refrigerators. You’ll need to collect a bunch of images of two-door refrigerators and put them in a folder, and then a bunch of images of three-door refrigerators and put them in another folder.
  2. Train your model. Now, whatever data sets you collected in the previous step, you upload them and the AI cloud will train the model based on that dataset. The fact that you already separated the images is all that’s needed for training. Once the new data model is trained, you will receive its ID.
  3. Use it for prediction! You can now get predictions on images that the model has never seen before. It’s as simple as making an API call using the new model ID.

Computation: Einstein Language

If images aren’t what you need to process, chances are you’re looking to train using text. For this, Salesforce’s AI cloud has Einstein Language, which is currently made of two services: Einstein Intent and Einstein Sentiment.

  • Einstein Intent is a general classifier of the natural language processing (NLP) type. It allows you to define your own classes and upload data that represents those classes.
  • Einstein Sentiment is a pre-trained model which is able to analyze human language to derive the feelings of the content and surrounding users’ statements and classify those into positive, negative, and neutral classes.
Einstein Language's model makes natural-language AI cloud training and classification easily accessible to your Salesforce Einstein app via an API.

Einstein Language services work just like our image classification example. Here, we will also define classes.

In the above example, the intent is about routing cases. Every time a case comes through, we want to analyze that and route it to the right department: Shipping, billing, product, sales…we can define as many classes as we need. But in the case of Einstein Sentiment, the classes are fixed, so we just have positive, negative, or neutral.

Once you have separated the data into different classes, you can train your models. Training the model is very easy with the provided API. Just like with Einstein Vision, once the model is trained, you’ll have your model ID and you’re ready to get predictions.

Salesforce Einstein: Off to a Smart Start

Now that you have a taste for the possibilities that Salesforce Einstein brings within your reach as a Salesforce developer, and how easy it would be to help your clients or employer leverage the AI cloud, getting started merely requires setting up an account. We look forward to hearing how you’re using the AI cloud to revolutionize your own app!

This article written by Fahad Munawar Khan and is originally posted at Toptal


About the Author:

Fahad is a Salesforce Certified developer with 5 years of experience working on various client engagements in the web technology area. He takes pride in creating solutions that not only satisfies the requirements, but also makes the best possible usage of all Salesforce platform features and resources.

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