It is undeniable that Artificial Intelligence (AI) has been changing our lives for the better and one of its famous abilities is to bring natural conversations to life. People have been familiar with chatbots for a while now, but voice assistants can still be a relatively new concept for some.
In this article, with consultancy from Dr. Thomas Forss – co-founder and CEO at StageZero Technologies, you’ll learn about the concept of voice assistants and go behind the scenes of one of our customers who are specialists in this field. We will also share with you our most crucial points to consider regarding the training data needed for the development of your voice assistant model.
What is a voice assistant?
Maybe you’re already familiar with Siri by Apple, Alexa by Amazon, or Google Assistant. These are all examples of voice assistants. Most of the voice assistants consist of four different components. The automated speech recognition component that translates the speech we provide into text. The Natural Language Processing component trains the machine to understand what we are saying and identify commands and intents. The software assistant within the voice assistant processes different commands or intents. And finally, the text-to-speech component allows the voice assistant to talk and to respond to what we are asking.
Then, there are two types of voice assistants: conversational, and command-based. Examples of the conversational type are, again, Siri or Alexa, where we can have conversations with the AI, which mimics human interaction. This is what we’ve been familiar with from movies and real life: we have a conversation with the AI, and it instantly answers the questions we ask. The other, command-based voice assistants don’t allow conversation. Instead, you just tell the ‘assistants’ what to do and they execute what you want!
How StageZero helped our customer to improve their voice assistant solutions
One of StageZero’s customers has been developing a voice assistant specifically for delivery drivers. Their voice assistance solutions aim to augment the daily workflows of mobile workers, deployed as driver apps and in scanners or vehicles. For this project, we used our own unique technology to provide the customer with speech recognition training data. This data is used in their machine learning processes to empower their AI systems.
This is a typical case for StageZero as we help businesses collect, annotate, and validate data. It’s always important that the data from the collection process be diverse. For annotation, it depends on the case: in some cases, you need a full transcription on the data and in other cases, you may need to use an intent classification. Finally for validation, we ensure that the data that enters the system is correct and meets the required quality standards. We even provide model development in cases where the teams don’t necessarily possess the relevant capacities in-house.
Voice assistants and data
When it comes to data for voice assistants, there is famously a lot to consider. Having been working with voice assistants for many years now, we have three important key take aways for you when considering data for voice assistants:
The first point is basically ‘common sense’ in the AI development field but may also be the most important point: for it to be usable, the data should be representable of the end-user conditions. In other words, the data should be diverse enough to cover all the users that are going to be using your service. Examples include age range, gender, nationalities, languages, dialects, etc.
Secondly, quality is more important that quantity. It’s more critical to have unambiguous labels than to have a lot of data. Unambiguous labels help you ensure that there is no overlap, especially if you have a smaller data set. If you have a larger data set, there might be less problems - however, it is still ideal to have both unambiguous labels and a large amount of data.
The final advice for you to consider is: when you start developing a voice assistant using an iterative approach, either from a software development perspective or from a data perspective, you may benefit from starting out small and learning from the first batches. Once you have learnt what you need more of, you can then expand the scale of the data set. This way, you will save a lot of money when developing.
Would you like to learn more about how StageZero Technologies can provide training data for your voice assistant model? Speak to our partnership team here.
Helsinki, Finland - April 4, 2022 - StageZero Technologies today announced it has joined NVIDIA Inception, a program designed to nurture startups revolutionizing industries with advancements in AI and data sciences.
StageZero Technologies is an AI data growth company based in Helsinki, on a mission to accelerate the AI revolution. Founded on ethical principles and operated by AI industry experts, the company is now one of the only EU-based and GDPR-compliant AI data providers.
StageZero Technologies offers enterprise solutions for all aspects of data in AI projects. The company specializes in Natural Language Processing, computer vision, and custom algorithms.
NVIDIA Inception will help StageZero increase visibility to established and emerging markets, provide the company with valuable insights on its market segmentation and trends, and further enhance its product offerings. Inception will also offer StageZero the opportunity to collaborate with industry-leading experts and other AI-driven organizations.
“Unlike traditional accelerators, NVIDIA Inception supports all stages of a startup’s lifecycle and works closely with its members,” states Thomas Forss, StageZero’s CEO and co-founder. “The program helps startups like us evolve faster through access to cutting-edge technology and NVIDIA experts, connections with venture capitalists, and marketing support to reinforce our company’s visibility.”
NVIDIA Inception helps startups during critical stages of product development, prototyping, and deployment. Every Inception member gets a custom set of ongoing benefits, such as NVIDIA Deep Learning Institute credits, marketing support, and technology assistance, which provides startups with the fundamental tools to help them grow.
About StageZero Technologies
StageZero Technologies is an AI data growth company based in Helsinki, on a mission to accelerate the AI revolution. Founded in 2016 by Thomas and Nicklas Forss, StageZero is one of the only EU-based, GDPR-compliant AI data providers. StageZero uses a revolutionary GDPR-compliant data sourcing process to increase the speed and reduce the risks in users’ AI projects. The company focuses on serving enterprise customers with data solutions tailored to support their roadmap.
StageZero is proud to announce our latest partnership with German Autolabs. German Autolabs is a Berlin-based company that constructs powerful automotive voice assistance solutions for professional drivers, couriers, and delivery teams. For this project, StageZero uses our own unique technology to provide German Autolabs with speech recognition training data. This data is used in the machine learning process to empower their AI system.
“German Autolabs builds voice assistance solutions to augment the daily workflows of mobile workers, deployed as driver apps and in scanners or vehicles. Sophisticated Intent Recognition and Natural Language Understanding are critical for us and having large corpora of natural language data is the foundation of high-quality semantic and language models.”, explains Dr. Christoph Neumann, CTO at German Autolabs.
“Partnering with StageZero has been vital in providing us with high-quality utterance corpora for training our proprietary language and semantic models. Delivery speed, variations and naturalness of the utterances provided by StageZero's unique technology are unmatched by more traditional data collection methods.”
“This is a prestigious deal for StageZero where we have been delivering a large amount of speech data. We are proud to have succeeded in meeting German Autolabs's high technical requirements specifically to do with Natural Language Processing and look forward to this long-term cooperation. We hope that our support for German Autolabs contributes to their mission of increasing efficiency and improving quality of service in many transport industries.”, says Thomas Forss, CEO and co-founder at StageZero.
Listening - or hearing - is a natural human ability, next to seeing and talking. In other words, humans do not need to learn or practice hearing things. As young children, we start by hearing sounds around us: people talking, dogs barking, music being played… Growing a bit older, what we perceive through hearing turns into our understanding of language.
Although this process of language comes instinctively and naturally for humans, it is not the same for computers and machine systems, whose native language is machine code rather than words.
Language is considered one of the most complex forms of data, with different semantics and exceptions which are extremely difficult to understand in case of missing intent and context. Therefore, it is not a surprise that it has taken decades of our efforts to train AI to ‘learn’ human language. The good news is, machine learning’s capabilities are constantly developing, which enhances our opportunity to advance Natural Language Processing (NLP).
NLP can be seen as a branch of artificial intelligence (AI) that deals with the task of providing computers with the ability to understand spoken words and texts in relatively the same level human beings can.
During processing, language is divided into different parts by the NLP software to be interpreted and understood. This can be in the form of speech or text, depending on the software used. NLP integrates computational linguistics – rule-based modeling of human language – with statistical, machine learning, and deep learning models. These technologies combined allow computers to process human language in the form of either text or voice data, and to understand its meaning, including the intent and emotion.
NLP runs programs that translate languages, summarize large text data promptly, and respond to voice commands. Examples include voice-to-text dictation software, speech-operated GPS programs, customer support chatbots, digital assistants, and many other consumer convenience products. You may already be familiar with your Siri, Alexa, Google, or any other virtual assistant out there. Such technologies have taken us decades to develop thanks to advanced AI.
Moreover, NLP also plays an important role in business solutions that support the streamline effort of business operations, boost employee productivity, and reduce complexity of processes.
NLP is the driving force of AI in a lot of modern real-life applications. Examples of NLP solutions which StageZero are offering as services include language collection, data labeling, data categorization, verification and augmentation for both text and speech (audio) products.
As specialists in speech data creation for virtual assistants and other applications, we enable you to reach over 20 different small and medium languages throughout the world, which can be used for multiple projects, such audio recordings. Audio services involve having hundreds of users read and record sentences to improve voice recognition services. We give you access to over 10 million such users. These users can also be used for text labeling of handwriting, math, drawings, and more. Text labeling can improve functionality of chat bots, or give new insights into sentiment analyses of, for example, your brand perception among your customers.
Did you know that today – the 28th of January is a very special day?
At least it is special not only for the Council of Europe, but for the entire global Data Protection community, and above all, for every individual protected by this crucial right. In 2006, the Committee of Ministers of the Council of Europe launched Data Protection Day, to be celebrated each year on 28 January. Data Protection Day is now celebrated worldwide and is called the "Privacy Day" outside Europe.
On 28 January 2022, the 16th Data Protection Day is celebrated globally. The event’s main goal is to raise awareness on individuals’ rights to data protection, how to execute those rights, and to educate citizens on data protection challenges. The Council of Europe continues to take the leading role by showcasing and supporting initiatives held on this occasion.
Initially, Data Protection Day only focused on informing businesses and their users about the essentiality of data privacy and protection of personal information online, specifically on social media platforms. Besides educational purposes, Data Protection Day also promotes events and activities that empower the development of technological tools that reinforce individual control of personally identifiable information, stimulate privacy laws compliance, and foster discussions between stakeholders with the main subject of enhancing data privacy and protection.
StageZero Technologies is proud to be one of the only EU-based and GDPR (General Data Protection Regulation) compliant AI data providers. We understand the importance of Data Protection both internally at our company, and externally – what it means to our customers, and how to protect their data.
“Our process for collecting and labeling data is developed to be GDPR compliant. This means we have the appropriate documents such as privacy and processing agreements and we do not share identities of people so that data stays anonymized. For example, when collecting speech data for speech recognition we do not share any identifying information about the user.”, explains Thomas Forss, CEO and Co-founder at StageZero.
“From having legal counsel validate regulatory compliance of our strategic solutions for each customer case and creating a data protection impact assessment (DPIA), to maintaining a data security policy for the case - we follow our own process to ensure that we produce privacy compliant data.”, Lesley Kiernan, Business Development Director at StageZero added.
To find out more about how StageZero became specialists in this field, ask us about our biometric anonymization services.
We are so excited to announce our latest seed funding led by Konvoy Ventures!
In 2021, StageZero Technologies raised $1.8 million in funding to concretize its journey of accelerating the AI revolution. The funding supports us to expand our team, improve our product efficiencies, and continue innovating tools that empower organizations to ethically develop AI systems quickly and easily.
The round was led by Konvoy Ventures with participation from Turkish venture studio Ludus, blockchain leader Hyperamp, and existing investors Into Ventures, Nordic Game Ventures, Alexis Bonte (Stillfront Group Chief Operating Officer), Andrew Sheppard (Managing Director at Transcend Fund, and Rakuten Games board member), and Wilhelm Taht (Senior VP & GM of GSN). Konvoy Ventures Managing Partner Jackson Vaughan has also recently joined StageZero’s board.
Until now, the AI revolution worldwide has been bottlenecked. The industry has been facing a distinct shortage of usable datasets for ever-increasing use cases. The data required to train machine learning systems is commonly expensive, difficult to attain and label, and is delivered with extremely long wait times. On average, 80% of time spent in AI projects is spent on data activities. Compliance with privacy regulations is complicated, time-consuming, and costly.
Despite these obstacles, Memory Leak predicts that the AI training data market will be worth $5 billion by 2023, with the industry growing 31% year on year. StageZero’s products are leading the revolution by removing bottlenecks currently standing in the way of such growth.
StageZero’s solutions solve the bottlenecks by reducing the time spent on data activities by 40% and providing high-quality and GDPR-compliant data quickly and reliably for a wide range of use cases. Our benchmarks show that our solutions have higher accuracy than industry-standard services, and at a third of the cost.
Founded in 2016 in Helsinki, Finland by brothers Thomas and Nicklas Forss, industry experts in machine learning and engineering, StageZero inserts in-app tasks into mobile games; bite-sized activities that users can complete in exchange for game rewards and perks, such as coins and extra lives. Effectively, they are an alternative to incentivized video ads.
The in-app tasks typically require gamers to label items by drawing boxes around specific objects or providing simple audible translations using their own voices. This way, gamers’ inputs contribute to generating valuable AI training data for both computer vision and natural language processing (NLP), the two main specialisites of StageZero. Besides mobile games, StageZero also partner with e-learning platforms to gain input from highly motivated reward seekers.
“We are accelerating the AI revolution, with our technology providing value across the entire chain. With our technology, companies can source AI training quickly, while gamers have their user experience enhanced,” explains CEO Thomas Forss. “By harnessing the power of mobile gamers, a segment which continues to grow rapidly, we will drive rapid, yet ethical, technological progress through data, and with this funding we are now ready to significantly expand our team and refine our product.”
Lead investor Konvoy Ventures Managing Partner Jackson Vaughan recently joined the board, and explains his enthusiasm for StageZero’s unique approach. “The data labeling marketplace that StageZero is building for game studios and data science teams provides a better experience for players, a new and potentially more lucrative revenue stream for mobile games, and a scalable labeling solution for teams building machine learning models. We’re thrilled to back the Forss brothers and the rest of the StageZero team as they bring micro tasks to gamers all over the world.”
This investment brings us to a total of $2.8 million since being founded in 2016 and we’re excited to use to extend our services to support even more clients around the globe.
It is undeniable that the use of Artificial Intelligence (AI) and machine learning are now one of the most popular topics when it comes to the future of business. AI has been revolutionizing how organizations operate. Therefore, it is vital to kickstart this exciting integration by learning how to make AI work for your business, as this will construct a performance system that ensures your organization evolves efficiently.
In most cases, AI acts as a form of Business Process Automation (BPA) as its solutions typically include repetitive, time-consuming assignments and generating methods to accomplish them efficiently.
Although BPA often deals with highly repetitive and predictable tasks, evolutions in computing technologies have significantly widened the range of automatable subjects, such as automation of texts and images. Still, framing AI solutions this way can help to configure the integration approach for your business in a more familiar manner.
Nowadays, technology has advanced to allow us to build AI systems that, for example, impressively identify objects in images, or perceive the meaning and emotion of texts and voices.
However, the ability of AI systems is nothing transcendent, rather it is all about their journey of being trained to recognize data patterns and then automatically processing other data much more quickly and on a much larger scale. AI-powered automation is often distinguished from general BPA by the fact that AI is trained, rather than built.
Effective AI integration requires sufficient training and realistic expectations. Machine learning simply indicates the process whereby machines learn - AI systems’ performance depends heavily on the quality and quantity of data they have been trained with.
It is common when discussing automation or AI, to relate these concepts to incredibly fast and accurate processes. However, it is important to understand that tasks such as annotating texts and images are complicated for machines and AI systems – they also need time to learn through training data. In other words, your business can benefit from AI from the start, but the best results certainly take time.
Instead of expecting fast and efficient automation straight away, it is vital to integrate AI to your business process step by step. Executing this incremental approach will also help you to decide which tasks should be automated and which should be given to humans.
It is important to note that to evaluate AI systems’ performance, we should use different strategies from those that we often have for human workforce’s performance. This is due to several key reasons:
Firstly, humans and machines operate differently, especially since AI systems can function at a much larger scale than humans do, so there must be differences in evaluation approaches.
Secondly, again because humans and machines work differently, the types of error made also vary. It is interesting that machines would often create errors that humans would not and vice versa. With this in mind, too much concern for minor errors made by AI can cause you unnecessary distractions. Instead, it is recommended to consider evaluating your system at a greater scale by selecting between two techniques: cross-validation and creation of a specific, large test set of products to assess the AI model’s predictions.
In order to keep up with the constant evolution of everything from your business growth to the training data your business is using, and to sustain the accuracy of your AI models, there is an important need to execute ongoing assessment and maintenance.
The first and most critical ongoing maintenance task is to keep your training data up to date. New data guarantees that new industrial trends are accurately and timely addressed and accessible to the system. It is also essential to ensure the consistency level of the labels used for your data and to update or remove old data in a timely manner to prevent confusion for your system.
Human-in-the-loop (HITL) learning can be seen as one of the key steps in the whole AI integration process. With HITL learning, the AI model can request human input on specific items which it is not sure of, meaning human workforce is used very efficiently here and the system receives the exact data it needs.
To wrap up, AI has been opening up an array of technologies that helps businesses make quicker and smarter decisions with the use of data collection and processing, hence stimulating efficiency and profitability. It can take time for businesses to familiarize themselves with the concept and application of AI, but it is worth-while. Now is the time to invest in AI.
Artificial Intelligence (AI) and machine learning models require access to high-quality training data in order to learn. It is important to understand the processes of effectively collecting, developing, and testing data as it helps to unleash the full potential of AI.
AI and Machine Learning are some of today’s fastest growing technologies. Many companies around the world are working to deliver applications that harness the power of AI to automate a wide variety of processes, and are using AI to increase their efficiency. To power AI models based on machine learning principles, a training data set is typically used to support machine learning process with reading or identifying a specific kind of data. This data is available in multiple formats including text, number, image, and video formats, to predict learning patterns.
Simply put, machine learning algorithms learn from data. They identify relationships, generate understanding, make decisions, and evaluate their decisions based on the training data they are assigned. The better the training data is, the more accurately the model executes its job. In short, the quality and quantity of the machine learning training data determines the level of accuracy of the algorithms, and therefore the effectiveness of the project or product as a whole.
AI datasets typically present in rows and columns, with each row containing an observation. This observation can be in the form of text, an image, or a video. It is not enough for your dataset to contain a large amount of well-structured data, unless these data have been labeled in the required way.
For example, self-driving vehicles do not only need pictures of the road, but they specifically need labeled images where important elements such as cars, bicycles, pedestrians, street signs are annotated. Another example would be with chatbots, which require entity extraction and high-quality syntactic analysis, not just raw language data.
In short, the data used for training usually needs to be accurately labeled or enriched. There might also be the need to collect more data to power the algorithms.
To decide how much machine learning training data is needed, you need to consider various factors.
The first one would be the importance of accuracy. For some algorithms, it is enough to have an accuracy rate of about 85 - 90%, while for more complicated algorithms, a higher accuracy rate would be required.
In general, use cases that are more complex usually require more data than ones that are less complicated. The more classes you want your model to identify, the more examples it will need for that task.
More and higher quality training data definitely improve your models. More training data means more information for your models and therefore a higher accuracy level, which is always needed especially for large-scale business practices.
Machines don’t see things as humans do. For example, when looking at a picture, we recognize that it shows a carrot. However, a machine would only see a series of pixels that has colors of orange and little bit of green, until it is given enough labeled images that tell it these specific pixels create an image of a carrot.
This is why the most efficient way to prepare the features and labels of training data so that models work successfully is to use human power. Typically, there is a need for a diverse group of annotators, even field experts in some cases, who do the job of labeling data correctly and efficiently. Besides labeling data, humans also help with verifying or correcting a machine’s output, for example ‘Yes, this is a carrot.’ This is called ground truth monitoring and belongs to the iterative human-in-the-loop process.
The more accurate training data labels are, the better the model will perform. Therefore, it is always ideal to find a partner that can take care of the often time-consuming data labelling process by offering data annotation tools and crowd workers. StageZero Technologies is a reliable partner.
In most cases, the process of building a model requires dividing labeled datasets into training and testing sets, training algorithms, and evaluating their performance.
When the validation set’s results are not what you are aiming for, you might need to update weights, add or remove labels, test out different methods, and retrain your model.
During this process, it is vital that your datasets are split in the exact same way, since this is the most efficient way to evaluate success: you are able to observe the labels and decisions which have been improved and which were unsuccessful. Using the same training data sets helps you to ascertain whether you are really improving or not.