DDD is helping leading Agricultural Technology companies that are innovating in modern farming. We will be in World Agri-Tech to meet leading farmers, growers, leaders, scientists, and more.
Interested in speaking/meeting with us? Setup a time here.
World Agri-Tech Innovation Summit 2023 Read Post »
We are Silver Sponsors at the 2023 CVPR Conference. This year’s conference will take place at the Vancouver Convention Center and gathers thousands of professionals, students, and leading organizations for a week of discovery, learning, networking, and more.
Visit our booth and see how we’re helping deliver successful Computer Vision programs. Or schedule a time to talk to us about your project by clicking the button below.
We are excited to announce our Gold sponsorship for the 2023 Autonomous Vehicles forum! The event will take place April 17-18 in Los Angeles and will connect leaders in automated vehicle technologies from around the world. Schedule a time to come by our booth and see what’s new!
Autonomous Vehicles USA 2023 Read Post »
ML CONF NYC is a one day event that happens every year and you can always find DDD here. The conference gathers professionals across many industries and professions to network and learn about all things Machine Learning.
Interested in speaking/meeting with us? Setup a time here.
By Aaron Bianchi
Updated Feb 6, 2023
Computer vision in retail has become a necessity for most companies in today’s times. To give their customers a better and enhanced experience, retailers are adopting computer vision-led solutions. Moreover, it also helps retail businesses with shelf space management and customer behavior analysis. With so many advantages, computer vision has truly modernized the way retailers sell and the way customers purchase. What all can happen with retail AI? Let’s find out.
Computer vision is a technology of computer science that focuses on human vision and its replication to help computers see and identify objects around them, just like human beings do. In simpler words, computer vision is like replicating the functions of the human eye in a computer.
It is as interesting as it sounds because its application in multiple industries is beneficial, not only for businesses but also for consumers. It makes every kind of process and experience faster and smoother. Whether it’s face recognition in your smart home or retail stores without cashiers, everything is so advanced with computer vision that not using it might slow down your life.
Talking specifically about retail, isn’t it interesting that everyday work like inventory management can become a lot easier? What other advantages does the application of computer vision have for the retail industry? Let’s explore.
Computer vision can help upgrade a customer’s journey by improving store layouts based on real feedback and data. There’s no need to rely on “projections” anymore as you have actual customer data to help you define their experience.
With the e-commerce boom, how do you attract and retain customers for a retail store? A retail store is competing with online shops that take just a few minutes to give the customer what they want. The customer checks out in no time too. If you replicate this experience in a physical store, you keep your customers happy.
In the retail industry, computer vision is used in various ways like self-checkout, virtual mirrors and autonomous robots among others. We will discuss 12 applications of computer vision in retail to give you a clearer picture.
Cashierless Stores
Customers can now enjoy “Self-checkout”. No hassle of long waiting times and reduction in human error in billing is now possible, all thanks to solutions that come from computer vision. The new age deep learning technologies can automatically detect product prices and calculate the bill.
Virtual Mirrors
Virtual mirrors provide unparalleled personalization options that boost customer experience in retail. It is a traditional mirror that has a display behind the glass. These virtual mirrors have computer vision cameras that help them display a broad range of contextual information to the consumer. For example, a fashion brand’s virtual mirror can have the technology that allows the customer to see various outfit options that will suit them without even trying them on physically!
Targeted In-Store Ads
Computer vision in retail has the ability to help the shops recognize and analyze buying patterns in returning customers. This is a powerful tool that allows businesses to send customized discounts or relevant Ads when these customers enter the store. Their purchase history metrics also allow the store to recommend products that will appeal more to the buyer increasing the likelihood of a sale.
Inventory Management
With computer vision, retailers can automate their inventory count which helps them update their inventory system in real time. Customers expect to know the availability information of products beforehand, so this feature greatly enhances the customer delight level. Think about it. Who wants to visit a shop only to find that the item they’re looking for is out of stock? You’ll do them a favor and you’ll do your business a favor too by not losing out on that sale.
Customer Behavior Analysis
Computer vision helps retail stores count the number of shoppers every day and study their overall behavior. From calculating the total time spent with each product to how much time buyers spend in the store, retailers can keep improving their sales strategy with the help of computer vision.
Store Layout Improvement
Cameras with computer vision can map customer movements and identify “hot areas” where customers spend the most of their time. This helps retailers to manage the overall layout of the store and maximize customer experience preventing early walkouts. From better product placement to focusing their discounts and deals in specific areas, retailers can now improve their store layout to meet customer needs, all thanks to computer vision.
Barcode Scanning Smartphone Apps
A lot of people trust the online shopping experience more because they have easy access to product reviews. This helps them make more informed decisions instantly. When it comes to physical stores, you walk in and you like a product, buy it and walk out. The next thing you know is that the product has horrible reviews and it turned out to be a complete waste of your money. Nobody wants to be in this situation.
Computer vision gives physical stores the ability to showcase reviews as instantly as online stores. There are barcode scanning apps which help customers scan the barcode of products via their smartphone cameras and receive all the information and reviews about the product.
Customer Mood Tracking
Computer vision can detect the customers’ mood during their shopping journey. For example, Walmart has already introduced a facial recognition system which helps cameras detect annoyed customers via cameras at the checkout point. If such a case is detected, a store personnel can salvage the situation by talking to the customer about what’s bothering them. This helps show the customer that the store cares about how they feel and they’re ready to resolve any grievance that the customer may have.
Supply Chain Management
Just like inventory management, supply chain management can become a seamless process with the help of computer vision. With the availability of data like the sales history of products, customer demands, trends, promotions, weather, etc AI can be used for effective restocking. This leads to fewer things going unpicked while there’s enough available for those who want more of a particular type of product.
Price Predictions
Based on specific demands and trends, launch dates, and characteristics, a retail business can predict the pricing of a product. This technology can be used in retail by creating a tool or app that helps customers know the price changes and upcoming price trends for a product. This feature is easy to build with the help of artificial intelligence and can help a brand to build customer loyalty.
Price Adjustments
AI Applications for retail stores can help stores visualize and try multiple pricing strategies. Once all the information about other products and promotions, sales, etc is collected, computer vision can help businesses prepare their best offers to acquire new customers. This flexibility in changing the pricing strategy based on actual information can be a great way to scale one’s business and wouldn’t be possible without computer vision.
In-Store Advancement
There are many other things in a store that can be revolutionized with the help of computer vision. Some retailers use The Kroger Edge technology that eliminates paper price tags and replaces them with smart shelf tags. This technology also helps with video ads and promotions on display screens. Other such in-store technologies and bots use translation for different languages to help assist customers from various regions.
With so many amazing and helpful things that computer vision does for the retail industry, it surely does solve a lot of problems faced by both businesses and customers. Here are nine such challenges that computer vision eliminates.
With accurate estimation of supply chain expenses at every level there is less chance of extra expenditure and losses in the process.
This correct estimation of supply chain expenses also lowers freight costs for third-party associates. This helps them prevent losses and ensures long-term relationships with their business partners.
Analytics and prediction of trends and changing prices saves businesses from overpricing or underpricing their products. This helps them reduce their chances of losses. For the customers, this comes as a delight as they have more competitive prices and products to choose from without having to settle for something they don’t like.
All the important information gathered via computer vision in retail from big supply chain datasets can be used for an effective retail decision-making process. Without computer vision retail decision-making was a difficult process as there was not much verifiable information available.
AI could be connected with other systems and departments within the business to improve the demand and supply planning and capacity management.
Computer vision helps in optimizing orders to accurately meet demand. This increases customer loyalty thereby reducing the number of irate customers.
Automating vehicles in the supply chain such as trucks and delivery robots increases efficiency thereby making some parts of the process autonomous.
Artificial intelligence when linked with GPS can track and help with better routes for delivery. This can improve the employees’ and the customers’ experience as deliveries can be faster.
When it comes to routing, AI can also plan all delivery operations for the business making all processes smooth and efficient.
If you’re a retail business that wants to be right at the top of your game and exceed your customers’ expectations, computer vision is your answer. That’s one method that lets you measure and analyze your growth while making your processes easier and faster. There’s no better way to scale your business and you’ll believe it when you use it. No idea where to start with AI implementation for your business? We’re just a click away.
The Future Of Retail: How Computer Vision Is Modernizing Retail Read Post »
By Abhilash Malluru
Feb 1, 2023
Autonomous driving is becoming more prevalent worldwide, garnering increased interest in optimizing technology through data labeling and annotation from investors and developers alike. With that growing interest comes an emerging need for experienced developers who can develop the tools and processes necessary for driver behavior monitoring, self-parking, motion planning, and traffic mapping.
Growing acceptance of autonomous driving has led to several approaches to advancing data labeling, annotation, and other machine learning processes. As these become standardized and more widely accepted in the industry, it’s crucial to understand the difficulties and obstacles which might arise in deploying them to any autonomous driving development platform.
The standard methods regarding the implementation of data labeling and annotation are as follows:
Bounding Boxes
Semantic segmentation
Polylines
Video Frame Annotation
Keypoints
Polygons
2D bounding box annotation uses video or image annotation to identify and spatially place objects. It first maps items to develop datasets, then machine learning models use those datasets to localize objects. Depending on the method deployed, it can support various tags or text extraction for things like street signs.
This annotation technique is vital for an autonomous vehicle or robotaxi’s navigation. It relies heavily upon complex logic systems and requires additional inputs to differentiate for decision-making, meaning it requires significantly large quantities of data and human input for the vehicle to operate effectively and safely.
Partnering with firms that have extensive experience in this method like any reputable managed service model (MSM) can help you implement and deploy a technique like bounding boxes. A managed service provider (MSP) has both a data annotation workforce and expert consultants who can help guide your needs and pinpoint any difficulties or obstacles that might arise.
Semantic segmentation is a technique that relies on a computer’s optical input to divide images into different components and label them by each pixel. This process is crucial to identify different types of objects so that a system can make a decision. For example, semantic segmentation helps a system identify people in a crosswalk. It may not know how many, but the point that people are crossing is enough to influence the decision-making process.
However, the most significant hurdle is that semantic segmentation is incredibly time-consuming. And this is where a dedicated team of SMEs from a third-party platform becomes invaluable. MSMs enable any organization seeking to implement semantic segmentation toolchains for this absolutely crucial process.
Since DDD’s workforce is trained in standard models and data annotation methods, they can help establish efficient and steady workflows while minimizing operational costs. These experts can handle such laborious tasks as semantic segmentation so you can place your focus elsewhere, ensuring you can complete other project needs before deliverables are due.
This image annotation method enables the visualization and identification of lanes, including bicycle lanes, lane directions, diverging lanes, and oncoming traffic. Polylines require extensive data sets to be successfully labeled and deployed.
Polylines are crucial for autonomous driving as a means of lane detection. Accurate and consistent modeling allows for navigation and the avoidance of obstacles. Plus, models can be trained further so they better adhere to relevant traffic laws by detecting road markings and signs. MSMs can help offload some of the enormous overhead which goes into developing the toolchains necessary for polylines.
Autonomous vehicles can use video annotation to identify, classify, and recognize objects and lanes. It can work in conjunction with techniques like semantic segmentation and polylines. Video frame annotation is necessary for more accurate object detection and works in conjunction with other annotation methods to provide accurate results.
Video annotation is time-consuming as it relies upon analyzing and data labeling thousands of video frames. Whether your platform is leveraging video and image annotation for autonomous vehicles or robotaxis, partnering with a third-party service can drastically reduce the time needed to implement this form of data annotation.
Data drives both autonomous vehicles and the development of the systems which guide them. Keypoints provide a frame of reference for objects that might change shape by leveraging multiple consecutive points.
As with most of the techniques related to autonomous vehicles or robotaxis, this form of data annotation is a very consuming and costly process. While much of the modeling that goes into what serves a self-driving vehicle needs elements of artificial intelligence or machine learning, a human component must still input the points on the sets processed for data labeling.
Nothing encountered on the road will remain static, doubly so for those using autonomous vehicles in metropolitan areas. With this type of data labeling, leveraging an organization with actionable domain experience like MSMs can help develop streamlined methods and toolchains. Cost is dictated per hour or unit, and DDD’s staff brings much experience in standardized data labeling and annotation methods.
Polygons operate like bounding boxes for visual data annotation. Irregular objects and accurate object detection greatly benefit from the implementation of polygonal data annotation. Polygonal annotation can have far greater precision than the bounding box method. When properly implemented, it helps detect things like obstructions, sidewalks, and the sides of the roads.
Polygonal annotation is a vital step in the autonomous driving model. Objects are very rarely uniform, and as such this method of annotation has a crucial function in making effective and safe models for the sake of detection and recognition. Its integration into your workflow comes from it being a time-consuming process. Compared to methods like bounding box annotation, it requires even more resources and time to correctly integrate. Engaging an MSM to help provide a platform can significantly reduce the time needed to implement this into your autonomous driving toolchain. Leveraging a third-party resource with actionable and proven experience can easily lead to greater precision in your detection model.
The past few years have made it abundantly clear that autonomous driving is here to stay, and leveraging another organization’s expertise into your workflow frees up valuable resources and manpower which could be better spent on other aspects of project development. Plus, we can’t ignore the time it takes to invest and develop these annotation methods.
So if you’re developing the technologies and models that power autonomous driving, it’s worth considering outsourcing at least some of the workflows to a third-party vendor. MSMs like Digital Divide Data (DDD) provide a platform to help you and your staff overcome some of the pitfalls of developing systems for autonomous driving.
Data labeling and data annotation alike are diverse and complicated fields of work. You can discuss your project needs and requirements with the DDD staff today. By partnering with us, you gain access to a developed platform that delivers exceptional results for your digital labeling and annotation needs. Let’s discuss your project requirements today.
How Data Labeling and Annotation Are Fueling Autonomous Driving’s Global Movement Read Post »
By Aaron Bianchi
Sep 20, 2022
“Check all the images that contain traffic lights.”
For some, these increasingly difficult CAPTCHAs are a source of endless frustration. But they give us something interesting to consider. If we prove that we are human by correctly identifying objects, how can a computer check our work? The answer lies in a domain of artificial intelligence called machine learning (ML).
Before CAPTCHA pictures get to you, data scientists train computers to recognize objects by providing lots of examples (training sets). If you’re wondering where those training sets come from, you’re right on the money! They come from a process called data annotation or data labeling.
Then, a model is developed to recognize specific objects. If the model is good, the computer can use it to identify the same objects in new pictures.
Artificial intelligence can’t create working models without well-trained data sets—garbage in, garbage out – this has always been the rule of thumb.
1. We Get the Big Picture
Imagine that you could talk to a computer to teach it new things. If you wanted to teach this computer to recognize a pest that is disrupting your crop yield, how might you approach this?
Chances are, you’d show it some pictures of pests you are interested in spotting and say, “Hey computer, look for these!”.
Machine learning works in the same way. Data annotation is like gathering the pictures you would like to show the computer and circling the important parts.
Unlike the computer, we understand the end goal of the model. We’ve likely defined, or at least have an understanding of its use case. As humans, understanding how the entire process works gives us an advantage when developing a data annotation strategy.
For instance, you can use your judgment to pick out a picture that wouldn’t be the best to include in the set. In this way, you’re telling the computer, “This isn’t a great example; let’s move on to a different one.”
This type of human logic is what artificial intelligence cannot yet replicate. The human side of understanding what the data means offers greater flexibility and understanding that create more substantial outcomes. Outcomes are not as strong with automated training set preparation.
2. We are Natural Language Processors
Natural Language Processing, or NLP, is the branch of artificial intelligence working to make computers understand human speech. We interact with NLP almost every day through “smart” devices.
“Hey Alexa, tell me more about Natural Language Processing.”
Like other areas of machine learning, NLP requires large training data sets. One type of data set consists of transcribed audio to train AI to turn speech into text. Another data set contains large amounts of text with annotations to highlight specific areas.
Both need humans to curate and pre-process the data before moving forward. As humans, we have an obvious advantage: we create and use language constantly. Human-powered data annotation for NLP is a great way to optimize model development.
The applications of NLP are endless. Sentiment analysis helps companies mine affective states or moods from customer messages/feedback. NLP can break down language barriers in unprecedented ways. This means people can communicate about weather patterns or pest attacks in real-time using different languages!
3. The Promise of Innovation
With so many advances in artificial intelligence and machine learning, we can be sure that our work is only getting started. AI won’t innovate itself, and researchers in computer science are the ones moving the field forward.
Of course, thinking about the importance of humans in the data preparation process does not diminish the role of technology—new software solutions to machine learning enter the market daily. Human innovation is needed to translate theoretical advances into practice.
An essential part of assembling a data annotation strategy is determining which tools to use and when to use them. Experienced professionals draw from experience to select the right tools for specific situations.
With so much raw data available in the agricultural tech industry, companies realize that the best solution is often a combination of software. Check out how machine learning has use cases across industries.
4. Data Annotation Professionals See the Process Through
Data can be messy. And let’s be honest: humans can be messy too! In the case of machine learning, this shared characteristic works to our advantage.
We need workers to clean data, address inconsistencies, and format data in a way that works for training AI. We use the term “data wrangling” to describe this process. Although “wrangling” may seem like a harsh term, it captures the actual amount of effort needed to prep data before use.
Part of the benefit of using a data annotation provider is that they can help you through the entire process. This includes:
data creation or collection
data cleaning and curation
data labeling or annotation
Consider using artificial intelligence to detect potential disease in a large field of crops by periodically analyzing photos of crops. This is likely a massive undertaking for an organization. First, enough data to compile a training data set is needed.
Once you’ve created a clean training data set for supervised learning, the story isn’t over.
Human intervention is needed to assess how well the AI can correctly identify diseased crops in the future. In situations where the machine cannot perform accurately, people need to determine the parameters of a new training set. Then, the process repeats, once again under human supervision.
Harness the Power of Data Annotation
With machine learning driving global industries forward, organizations need access to high-quality training sets. Organizations might not have in-house resources to handle data annotation at scale.
Fortunately, Digital Divide Data offers across-the-board support to get companies to the finish line, no matter where they start. As a non-profit organization, DDD is challenging the industry’s status-quo with impact sourcing, youth outreach, and more.
To get started, see how DDD’s suite of fully managed services (CV, NLP, Data and Content) can exceed your expectations.
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4 Advantages of Human-Powered Data Annotation vs Tools/Software Read Post »
By Aaron Bianchi
Feb 23, 2022
We live in an era of sophisticated algorithms, Big Data, and machine learning that gets better by the day. Businesses recognize the importance of data processing, artificial intelligence (AI), and natural language processing (NLP) for growth. Here are some ways you may already be using NLP in your daily life that could inspire ideas for your company.
What is Natural Language Processing?
NLP is essentially AI that deals with understanding human language. Advanced language sets us apart from other animals on the planet, and communication is integral to our societies. So, as tools, computers were always going to have to develop to a point where they could decipher natural language patterns full of nuance. With the help of programmers and data scientists, machines are constantly refining their ability to comprehend subtleties and create meaning.
NLP Works in Three Fundamental Steps
Break down a spoken sample or written language input into parts or categories.
Discern how these pieces of information are linked.
Produce meaning.
The software detects context, emotion, and sentiment through exposure to lots of data. This consumption of enormous datasets is known as deep learning. Helped by developments in so-called neural networks that imitate neurons in your brain, deep learning only came to the fore in the 2010s. But it’s had a massive impact since then.
Using accumulated knowledge of word sequence and other factors, AI can interpret whether your use of bass refers to a fish or a guitar, for example.
NLP Applications You May Be Familiar With
Search Engines
Just Google it…When you Google something, the search engine offers you autocomplete suggestions. NLP facilitates these predictions by using search data to determine your intent and hasten the process. NLP also tries to overcome any spelling or other errors on your part and assembles relevant content in search engine result pages (SERPs) by matching your query to ideal web pages. In addition, semantic search can enhance digital marketing and SEO capabilities.
Virtual Assistants
“Siri, what is a virtual assistant?” If you’re like most people, you talk to your virtual assistants, like Siri or Alexa and even when you are on the line with automated call centers. Who wants to press numbers as options when you can state exactly what you want or are searching for? Do they sound monotonous or robotic, or are they unable to follow commands? In general, the answer is no, even though the tech has some way to go before consumer interactions become seamless. NLP divides your voice’s frequencies and soundwaves into tiny bits of code ready for further analysis. Speech recognition and voice recognition are two substantial aspects of NLP that will be major features of the online landscape in years to come.
Email and Document Assistants
“Great, thanks!” “Thank you.” “Got it.” Look familiar? Think about your smartphone keyboard and predictive texts that help you type faster, for starters. Consider, too, Outlook or Gmail’s Smart Reply functions.
You’ve likely worked with auto-complete functionality. Or you’ve used the grammar check browser extensions that abound on the internet, helping you craft professional messages or documents in the country-specific version of a language. Furthermore, your inbox can separate emails into various folders such as junk or promotional mail due to NLP.
Chatbots
“How may I help you today?” Chatbots, the text-based equivalent of voice assistants, have become popular and can fulfill basic requests such as booking flights or helping most customers answer simple questions. You might have come across one on an eCommerce store, during product demos, or on educational apps.
Customers often prefer texting or chatting with real people when the stakes are higher or when their needs are more complex. But as NLP improves, chatbots will become more fit for purpose.
Translation and Transcription Tools
“How do you say that in Spanish?” They perform the seemingly simple task of converting an input language into an output language or materializing spoken words on the screen. But there’s word order to manage, not to mention linguistic idiosyncrasies.
These days, you can point your phone camera at an object with a foreign language on it, and standard augmented reality apps on your phone superimpose a translation for you. The ingredients in products from overseas are no longer a mystery, and any included instructions should be understandable.
Life-Changing Use Cases
Future Possibilities! There are numerous current examples of NLP bridging information and communication divides significantly. Imagine an app that can translate sign language or serve non-verbal individuals with disabilities. NLP doesn’t just help us interact more efficiently with computers; it also opens up new and promising avenues with other people.
NLP Applications In The Future
On-demand TV streaming existed only in theory once, but steadily rising computing power and lower costs turned vision into reality. The same is true for our ideas about robots or internet of things (IoT) gadgets that can talk to us in a less stilted manner than we’ve come to expect.
Soon, home and work life might rely on integrated virtual assistants as much as they rely on video calls, GPS, or online shopping. Research firm, Gartner, suggests that by 2025 about half of all knowledge workers will interact with a virtual assistant every day. And the worldwide conversational AI market is projected to grow to $15.7 billion by 2024.
NLP can play a role but are not limited to these industries:
Banking
Healthcare
Media
Manufacturing
Retail
Currently, the automotive industry is testing voice biometrics so drivers can access info such as navigation history. And self-driving cars will require advanced NLP. Thanks to human innovation, NLP’s applications are endless.
Partner With Digital Divide Data
Digital Divide Data partners with Fortune 500 companies and world-class institutions, and can help you optimally sort through and organize your datasets. Using NLP, we can hone in on pertinent information in CVs to structure your training data. We hold ourselves to the highest standards and provide an end-to-end data service customized to your needs. Reach out for more information and to find out how we can strengthen your operations and brand.
Everyday Applications You Didn’t Realize Were Powered by NLP Read Post »
By Aaron Bianchi
Feb 3, 2022
Artificial Intelligence (AI) is growing in popularity as a tool to provide everything from better customer care to translation services, driverless cars, smart technology, and more. Consisting of several different technologies that work together to deliver the end result, AI is computer-based programming that mimics human behavior.
Although AI has advanced enormously over the past decade, involving humans in its development is still essential if premium results are required.
Here we take a look at how AI is trained using test data and how human-powered data annotation and data labeling adds significant value to the outcomes that AI delivers.
Data annotation software is software that is written to annotate production-grade training data. AI isn’t created in a fully formed state. To provide a human-like response to data, AI has to “learn”. As an example, when AI picks up an image of a tree, it doesn’t know that it’s an image of a tree. The ability to recognize that a particular configuration of pixels is a tree is only obtained after AI has had access to millions of tree images.
The process by which the AI learns to recognize a tree (as an example) is known as machine learning (ML). For effective machine learning to take place, the AI needs access to a large volume of training datasets – data that can be used to help develop the algorithms (mathematical models) needed to develop a human-like response. Using the data, AI can develop a prediction model on the basis of its learning.
For example, if an AI program has been given access to millions of tree images, it can use mathematical modeling to build a picture of what arrangement of pixels, statistically speaking, is most likely to be a tree. With this information, when the AI is given access to another tree picture, it can assess the probability of it being a tree and label it accordingly. Obviously, AI is capable of interpreting millions (if not billions) of different pieces of data, but to do so accurately, it needs access to enormous amounts of test data that provides the material needed to create accurate algorithms (mathematical models).
To assist in the process, the test data needs to be annotated – labeled in such a way that AI can interpret it effectively and developing a high quality training dataset, depends on many things. You can use platform providers or managed services with specialists. In the context of recognizing a tree, for example, data annotation might be used to enable the AI machine to interpret the data you’ve provided as a tree.
Due to the enormous amount of trained data, or training datasets that are needed for successful machine learning, data annotation software has been developed to try to reduce the time needed for annotation to take place. Data annotation software does make machine learning faster, but it also has some significant drawbacks, some of which are highlighted below.
Exceptions. Every set of data is likely to have exceptions – outliers that are likely to confound the boundaries set up as part of the algorithmic modeling that AI completes. If the data annotation software can’t recognize these outliers and label them correctly (which is likely if the data doesn’t conform to the usual parameters), this limits the level of machine learning that can take place.
Limited annotation labeling. Particularly when diverse data is being deployed, the software may not be able to cope with the large variety of labels that are needed for effective machine learning.
Quality control. Data annotation software is usually equipped with features that identify where there are quality control issues. Unfortunately, the issues identified are those that are beyond the capability of the annotation software to resolve. Without additional input, those quality issues will remain.
Limited sorting. Data annotation software can play a valuable role in sorting data, and flagging data that it can’t easily sort and label. Unfortunately, the software can’t correct the issues it flags – which is where human intervention comes in.
Humans can resolve issues with test data that data annotation software can’t. Although the goal of machine learning is to create AI that can “think” in the same way as a human (but without the risk of human error), it’s still not as advanced as the human brain. Particularly when it comes to making judgments that involve subjectivity, data that involves an understanding of intent is vital to get the best results. For example: a surgeon clutching a scalpel, could be considered interchangeable with a knife-wielding criminal, without the benefit of understanding intent.
The advantages that humans bring to data annotation software mainly relate to our ability to process data that falls outside the machine-learned parameters.
Humans are essential when it comes to developing the training datasets that can’t be successfully cataloged by the annotation software. More sophisticated decision-making, particularly that which is based on subjective criteria, needs human input.
When annotation software presents a quality control issue, it’s humans that are required to decide on a suitable course of action.
Similarly, diverse, complex data will need human intervention for it to be correctly labeled so that machine learning can take place effectively.
Ultimately, AI algorithms are only as good as their test data. The higher the caliber of the datasets (including accurate, clear labeling), the more effective the AI is going to be in meeting its outcomes.
As humans are the machines that control machine learning, their input is essential for the process to deliver optimal outcomes.
Why Data Annotation Software Still Needs a Human Touch Read Post »
