News

Catch up on our latest news, developments and insights

Synteda attended SAST VÄST Expo on October 9, 2019 in Gothenburg

SAST VÄST is one of the biggest software testing meetings in Europe. Synteda is a partner and sponsor of the event and we were thrilled to present HiveMind, the future of automated testing. During the event Synteda was holding a competition where participants were testing the prototype and sharing their experience. HiveMind captured attention from different business fields and we are pleased for the positive feedback.

Written by Synteda, 2019-10-21 Read more

HiveMind

HiveMind is the future tool that will allow augmented automatic testing. This tool records data and learn from human interactions during test sessions. It analyzes the test data providing the user with new and better ways to perform tests. Contact us to book a demo or to know more about HiveMind.

Written by Synteda, 2019-10-21 Read more

Synteda attended IoT Sweden's annual conference on October 10, 2019 in Uppsala

An online world makes it possible to make new decisions that lead to improvements in many areas. With the support of Vinnova, the Swedish Energy Agency and Formas, IoT Sweden has the task of running the strategic innovation program for the Internet of Things. Synteda thus takes a prominent position in future applications of IoT in Sweden. Synteda attended this conference to contribute with expertise in Artificial Intelligence and Machine Learning in many of the applications to come. Our on-site consultant was Anders Isvén, Innovation Manager.

Written by Synteda, 2019-10-21 Read more

Innovation

We would like to introduce you into an innovative journey. Synteda can help you to understand your costumers and create smart and innovative products or services to provide better experiences for you and your costumers.

Written by Synteda, 2019-10-21 Read more

Synteda joining Breakit Impact Challenge

We are committed to be a responsible and sustainable business. Synteda has created a travel policy to become more sustainable: As first alternative is to have virtual meetings to reduce carbon footprint and travel costs. The company office and conference rooms are located with easy access to public transport. Always choose the climate-smart transport, such as walking, cycling and public transport. Influence other organizations to travel enviromentally smart.

Written by Synteda, 2019-10-21 Read more

Synteda sponsors Sävedalens IF

Synteda has become a proud sponsor of Sävedalens IF. SIF is a football club that spreads the joy of sport at young ages and also together with SISU and Rädda Barnen (Save the Children) fights against offensive treatment, bullying and discrimination.

Written by Synteda, 2019-10-21 Read more

Synteda is providing IT trainings to Humanus Yrkehögskola

Humanus Yrkehögskola has a vast experience providing IT trainings. Their courses are lectured by experts with many years of experience in the field guaranteeing up-to-date and relevant knowledge. Our experts are successfully collaborating with Humanus mentoring IT trainings for the next generation of system developers.

Written by Synteda, 2019-10-14 Read more

Article by Stefan Byttner, CTO of Synteda talks about Artificial Intelligence

Read more: https://alivmagasin.se/det-ar-nu-det-hander/

Written by Synteda, 2019-10-14 Read more

Synteda has applied several AI and blockchain funds from Vinnova

We hope and believe our uniqueness and sustainable thinking will lead us forward in the competition.

Written by Synteda, 2019-09-03 Read more

Seminar by Stefan Byttner on Artificial Intelligence

Stefan Byttner, CTO of Synteda, will be conducting a seminar on Artificial Intelligence in Falkenberg on September 13, 2019. Event webpage: https://naringslivetfalkenberg.se/cal.php?event=203

Written by Synteda, 2019-08-15 Read more

Synteda is an active member of Engineers Without Borders

We believe that everyone should have access to quality education, sustainable energy, clean water and sanitation to improve the standard of living. We are an active member of Engineers Without Borders a non-governmental organization who strive to make an impact providing infrastructures for disadvantaged communities. We will support Engineers without borders in their activities financially and also by sharing our competence.

Written by Synteda, 2019-07-12 Read more

Breakit impact challenge

Synteda is a proud partner to the Breakit impact challenge for being more climate-smart and we also keep the sustainable focus for the company.

Written by Synteda, 2019-07-12 Read more

Blockchain, an emerging technology for the future

As blockchain is now emerging strongly, we are now working on a product that provide services based on this advanced technology. We have successfully discussed with our partners about providing service on a blockchain platform and we are now making decisions to take the next step. More information about this product will be available soon.

Written by Synteda, 2019-07-12 Read more

Synteda has become a sponsor of SAST VÄST

We are glad to announce that we've become a proud sponsor of SAST VÄST (Swedish Association for Software Testing). SAST regularly holds the forums and conferences within the subject area. We are looking forward to broadening our partner’s network and hope that this will lead to cooperation and joint projects with other companies in the branch.

Written by Synteda, 2019-07-11 Read more

Synteda has now several trainings regarding AI

There are different levels of education: - Basics for those who want to familiarize themselves with the world of artificial intelligence and machine learning. - Profound training that can give detailed overview of this advanced technology and help to gain new skills. Our trainings are focused on different target groups, from business owners and managers to software developers and architects. We are offering various formats: self-learning kit and in-class trainings. <a href="http://www.synteda.se/image/id/86" download="aidocument"> Download training brochure </a>

Written by Editor, 2019-05-21 Read more

Jan Bosch article about Machine & Deep Learning: Non-Critical Deployment

Starting with non-critical deployment of ML/DL components is critical to learn about the challenges the company might experience as it moves to the use of ML/DL components in more critical deployments where potentially exceptional, unpredictable and erratic behavior severely undermines customer value derived from products, solutions and services In our research (see reference below), we have identified that companies that have reached this step experience a number of main challenges. These challenges are associated with the four stages of working with ML/DL components, i.e. assemble dataset, create model, train & evaluate and deploy. Below we discuss the key challenge in each stage. During the first stage where the company needs to assemble the data sets, organizations experience three main challenges. The first is that data that would be needed for training and validation is spread out across the company and needs to be collected from a variety of different data silos. The associated challenge is of course that the data from different silos may easily use different semantics and schemes for similar or related data items. Second, in most cases, ML/DL models required labelled models for supervised learning. Although the company may have the data available, it often is far from obvious to deduce what labels need to be associated with each data item. Finally, available data sets typically are assembled for specific purposes, causing the data to not be representative of reality, but rather contain a significant over-representation of specific cases or data. During the second stage the engineers are concerned with creating a model that is aligned with the problem at hand and that generates the desired output, such as a classification or prediction. Whereas during experimentation and prototyping, any model that achieves a some level of accuracy is acceptable, in this step the model will be exposed to customers. This requires the quality of the model to be higher, but most companies lack the skills and competencies to improve on a basic model. Doing so requires the ability to analyze which elements, algorithms or layers in the model cause the lack of accuracy as well as the ability to take corrective action to address the problem. The training and evaluation stage is concerned with training and evaluating the model defined in the previous stage. The key challenge here often is the availability of data for training and evaluation. Although approaches such as k-fold cross-validation exist and more experienced data scientists will know how to use these, in practice the company is in the early stages of adopting AI/ML/DL solutions and the amount of available talent in the company tends to be limited. The deployment stage is on the receiving end of the challenges experienced in the previous stages and this frequently results in a significant training-serving skew. This means that the model performs significantly worse in deployment than in training. This is typically caused by a difference between the data used during training and the data served during operations. Concluding, companies evolve through a number of steps when adopting AI/ML/DL models. In this article we discussed the challenges that companies experience in the second step where the company deploys the first ML/DL models in non-critical parts of products, solutions and services. The main challenges are concerned with assembling labelled data sets of sufficient quality and quantity as well as the skills of engineers to improve under-performing models. These challenges may cause a significant training-serving skew when models get deployed. The purpose of this article was to outline the challenges in order to help companies adopt ML/DL solutions while avoiding the traps that we outlined. Machine and deep learning offer fabulous technology that can provide incredible results and benefits. However, it comes with significant engineering challenges some of which we have outlined in the above. Good luck! Reference: Lucy Ellen Lwakatare, Aiswarya Raj, Jan Bosch, Helena Holmström Olsson and Ivica Crnkovic, A taxonomy of software engineering challenges for machine learning systems: An empirical investigation, XP 2019 (forthcoming), 2019.

Written by Jan Bosch, 2019-03-19 Read more

Jan Bosch article about Machine & Deep Learning: Experimentation Stage

This week I got the opportunity to speak at the initiative seminar organized by the Chalmers AI Research center (CHAIR). The key message in my presentation was that working with artificial intelligence (AI) and specifically machine & deep learning (ML/DL) constitutes a major software engineering challenge that is severely underestimated by companies that start to experiment with machine and deep learning. Although I have discussed some of the challenges in an earlier blog post, we have continued to conduct research in this area and we have collected additional data concerning the specific challenges. In addition, we have developed a model that captures how companies typically evolve in their adoption of AI/ML/DL. In the figure below, we show the steps that companies typically evolve through. In this and the upcoming posts, I intend to discuss the challenges associated specifically with each step. This is based on an article that recently was accepted for publication in the proceedings of the XP 2019 conference. Figure 1: How use of AI/ML/DL evolves in industry As the figure illustrates, the first step that most companies engage in is experimentation and prototyping. In this case, the work on machine & deep learning models is conducted purely in-house and without any connection to the products and services that the company offers to its customers. The work with basically any ML/DL approaches follows the process shown in the figure below. In the simplest view, here are four stages, i.e. assemble data-sets (or data pipes), create models, train & evaluate and, finally, deployment. There are two iterative processes. The inner loop is concerned with the typical activity of creating a model, training it, evaluating it and then tweaking the model with the intent of increasing accuracy and reducing error rates. The outer loop illustrates the periodic or continuous retraining of models based on the most recent data and the subsequent continuous deployment of models into operation. Figure 2: The basic ML/DL development process Our research shows that companies experience various challenges in each of the steps of the process and that these challenges depend on the evolution stage where the company finds itself. For companies in the experimentation and prototyping stage, I’ll describe the key challenge in each process step. For the “assemble data-sets” step (which in later stages becomes the data pipelines step), the very activity of assembling the right data-sets for training and validation purposes often proves to be a significant challenge. Although all companies tend to drown in data, this data often has unclear semantics and the way it has been collected is often unclear, resulting in data-sets that are not necessarily representative of the operational data that would be used during operations. As a data point: a company that I visited recently claimed that more than 90% of all effort in the data analytics team went to assembling data-sets and setting up reliable data pipelines. Although easily underestimated, this is a major challenge. The “create models” step is concerned with creating ML/DL models that perform well for the data that the problem domain is characterized by. As a well performing model is highly dependent on the characteristics of the input data, any issues during the previous step automatically affect the quality of the model. In addition, especially in this early stage, often companies experience a lack of talent with experience, exacerbating the situation. The “train & evaluate” step typically struggles with the fact that establishing the problem specification and desired outcome as well as having data-sets that capture a solid ground truth that can be used as a reference for training and evaluating models. As a consequence, it can prove to be difficult to determine which model is superior as well as whether any of the models is of sufficient accuracy. Due to the nature of this stage, that is no deployment mechanism yet. The challenges with setting up a deployment mechanism are discussed in future articles discussing the higher stages in the evolution model. Concluding, the first stage in adoption ML/DL in your products, systems and solutions is concerned with experimentation and prototyping. During this stage, the predominant challenge is the establishment of data-sets of sufficient quality as a basis for model creation, training and evaluation. These data-sets need to be representative of the data that will, during operations, come through the data pipelines. Our research shows that companies struggle with data quality in this stage and the subsequent steps in the development process are negatively affected. So, get going with ML/DL yesterday, but focus your energy where it counts: high-quality data sets. Reference: Lucy Ellen Lwakatare, Aiswarya Raj, Jan Bosch, Helena Holmström Olsson and Ivica Crnkovic, A taxonomy of software engineering challenges for machine learning systems: An empirical investigation, XP 2019 (forthcoming), 2019.

Written by Jan Bosch, 2019-03-19 Read more

Synteda today announce that it is acquiring Auqtus

The acquisition will significantly strengthen Synteda’s position in all IT sectors through Visual GUI Testing and Augmented Testing technology that will enable accelerated growth and expansion of Synteda’s offering to its customers. Maycel Isaac, CEO and Founder of Synteda: “We are delighted to welcome Auqtus to Synteda, the complementary talents and propositions will help accelerate Synteda’s leadership in the area of Visual GUI testing (VGT) and Augmented Testing (AT). What Auqtus have achieved to date is impressive but together we can invest in realising the full potential of the business. By combining capabilities and leveraging Auqtus knowledge, with academic pedigree behind it, we are confident that we can offer even more innovative and flexible solutions to our customers.” Founded in 2015, Auqtus has established itself as a highly regarded provider of VGT tools based on smart algorithms and AI. VGT, as shown through academic research, is both applicable and feasible in industrial practice and provides positive return on investment compared to equivalent manual testing. However, VGT scripts, like all automated test scripts, are still associated with development, maintenance and execution costs. Auqtus advancements in the area of AT aim to mitigate these challenges even further and foster a new era of testing. An era where humans no longer only instruct tools what to do but rather where humans work along side AI-driven tools to improve their joint testing capabilities in Human-Machine symbiosis. Emil Alégroth, Managing Director Auqtus: “We are thrilled to join an industry leader like Synteda and believe this is a great strategic fit rooted in a shared mission and culture. We at Auqtus have developed and implemented some of the most advanced API libraries for VGT and AT in existence and are extremely proud of what we have accomplished. Together with Synteda, we have the exciting opportunity to further develop our capabilities and deliver even more services and value to customers worldwide to help them solve their everyday test- and quality-related challenges. We are now focussing on a smooth integration.” Synteda managing team: “We knew from the beginning when we met Emil, and rest of Auqtus founders, that this agile, passionate and innovative group with their challenger attitude we love, would be a great fit at Synteda. Together, we will have a rich product offering that allow us to serve even more customers and in better ways than ever before. We are excited about what we can achieve together.” *************************** About Synteda Synteda (www.synteda.com) is one of Sweden’s leading artificial Intelligence and cutting edge technology provider, which wants to revolutionise the human experience for all kind of users. Founded in Gothenburg, Sweden. Synteda gives its consumers the option to enhance their knowledge in machine vision by offers such as Education, advanced Assessments, teams, and product development. For further information, please contact: Maycel Isaac, CEO info@synteda.se

Written by Editor, 2019-03-14 Read more

Article by Stefan Byttner about Intelligent machines and systems

Stefan Byttner, CTO of Synteda, talks about machines and humans in a article for Halmstad newspaper . https://www.halmstad7dagar.se/

Written by Editor, 2018-11-22 Read more