In today’s digital age, where information is massively booming, ordinary softwares cannot handle the data because it’s Big Data. In this article, we’ll understand the main characteristics of Big Data i.e., the 3V’s, its technological advancements, applications, challenges, and data security.

Big Data: The 3 V's of Data

<span data-ccp-parastyle="Body A" data-ccp-parastyle-defn='{"ObjectId":"7533be1b-57fc-4ad6-a76d-1cb34d81fd53|8","Properties":[134233614,"true",201340122,"2",268442635,"22",335551500,"0",469769226,"Helvetica Neue,Arial Unicode MS",469775450,"Body A",469777841,"Helvetica Neue",469777842,"Arial Unicode MS",469777843,"Arial Unicode MS",469777844,"Helvetica Neue",469778129,"BodyA"],"ClassId":1179649}'>Note:&nbsp;Sebastiaan is&nbsp;the former senior director of IT at NXP and recently embarked on a new challenge at&nbsp;Splunk.&nbsp;In his role at NXP, he was involved in the setup of the AI Innovation Center and after leaving NXP recently, he remains involved in the AI Innovation Center organization.&nbsp;This interview was conducted during his time at NXP.&nbsp;<hr> ‘Whoever owns the data, owns the world,’ Sebastiaan Laurijsse,&nbsp;former&nbsp;head of IT at NXP, replies to explain&nbsp;what&nbsp;the incentive&nbsp;was&nbsp;for NXP to co-invest in the new AI Innovation&nbsp;Center. ‘It’s important to invest in the AI ecosystem. It allows us to develop knowledge skills, keep an eye out for new talent and maintain our position in&nbsp;the market regarding AI.’&nbsp;&nbsp;That position is impressive. NXP is one of the leading companies worldwide in developing AI on the edge. Their chips, whether microprocessors or microcontrollers, are installed in edge devices ranging from smart refrigerators to&nbsp;fitness wearables. Special AI capabilities installed on the chip collect and process data locally at very low energy costs. ‘In that low power high processing segment our MCU capabilities are really unique. For instance, we developed AI for a smart microwave that will use different heating intensities for different types of food on the same plate, so that all the macronutrients are kept intact.’&nbsp;The data that NXP’s smart chips collect is meant for their clients, the platforms that host the smart appliances. But NXP also has a data collecting proposition of its own, called MIFARE 2GO, a cloud service that combines AI solutions for smart public transit. Laurijsse: ‘We’re using NFC technology in combination with smartphones to develop things like smart turnstiles or digital chip cards for public transport that can be installed on your phone.’&nbsp;NXP also applies AI to its internal processes. Algorithms help the accounting and financial departments to improve their services. And&nbsp;Laurijsse’s&nbsp;former&nbsp;IT department has recently developed a predictive model that helps engineers working from home to identify IT problems. ‘Previously it took us 180 to 200 minutes to diagnose the cause of an IT problem, like bad&nbsp;WiFi&nbsp;or forgetting to renew a password,’ says Laurijsse. ‘The new system can show the engineer much faster where the problem most likely occurs, bypassing the human operator.’ The model came in handy during the pandemic, where the number of NXP employees working from home increased from 3,000 to15,000 in mere weeks.&nbsp;&nbsp;Last but not least, NXP is a big player in the automotive market where it develops chips, camera’s and sensors in fields ranging from battery management to object detection, &nbsp;paving the way for the self-driving cars of the future.&nbsp;‘A car is also a big edge device,’ says Laurijsse. However, the NXP manager is realistic rather than optimistic when it comes to autonomous driving. ‘The field is still in its infancy. Many people think we are already at level five [maximum driving autonomy], but it’s really more like level two. Sure, there are already self-driving cars, but they operate in a fully controlled environments. It’s like they’re driving on virtual rails. There are so many safety conditions that the only outcome is success.’&nbsp;Laurijsse sees a parallel there with the discussion about what AI actually is. Today AI mainly focuses on statistical analysis, collecting data to train systems to discover patterns and make predictions. That kind of AI requires tremendous computing power. But to Laurijsse real intelligence entails more than making predictions based on what a system knows, like the virtual rails of the driverless cars.&nbsp;&nbsp;&nbsp;<img src="https://images.wevolver.com/eyJidWNrZXQiOiJ3ZXZvbHZlci1wcm9qZWN0LWltYWdlcyIsImtleSI6ImZyb2FsYS8xNjE2NDkwNTQ4NDEzLUZvdG8gU0wgMi1taW4uSlBHIiwiZWRpdHMiOnsicmVzaXplIjp7IndpZHRoIjo5NTAsImZpdCI6ImNvdmVyIn19fQ==" class="fr-fic fr-dib">What do you consider to be real artificial intelligence?&nbsp;Laurijsse: ’Real AI can also make good decisions&nbsp;in new and untrained situations. In other words, better decisions with less data. To do this the system has to be enriched with a certain amount of knowledge and information, just like a human being. We have amassed so much knowledge that we only need one&nbsp;word to understand an instruction, instead of entire sentences.’ &nbsp;&nbsp;&nbsp;Is the field too much focused on statistical analysis?&nbsp;&nbsp;‘No, it’s just the way it is. We used to drive three&nbsp;kilometers&nbsp;on one liter of petrol, whereas today it’s much higher. New technologies often start with a surplus of resources, only to make things more efficient later on. It’s a natural reaction. But I also think that in the case of AI innovation is not blocked by knowledge and expertise, but by ethics, laws and regulations.’&nbsp;&nbsp;Ethics&nbsp;is a hot and as yet unsolved topic in AI. Who is responsible when a driverless car hits a pedestrian? In China jaywalking pedestrians already get an instant fine when spotted by a smart camera. And many AI scientists and engineers are protesting the development of AI weapon systems or ‘killer robots’ that can pull the trigger based on data.&nbsp;Should we build everything we can&nbsp;build?&nbsp;&nbsp;Laurijsse: ’You could argue that we should sacrifice privacy to improve efficiency, for instance in waste reduction. By combining different data sources you can do a lot of good. But there’s also the&nbsp;other side. Facial recognition can be used to catch criminals, but&nbsp;dictatorial&nbsp;regimes can also use it to suppress and spy on their own population. However, I feel that we can still improve in data sharing, because there are a lot of use cases that are extremely valuable if data was shared.’&nbsp;&nbsp;Could you give some examples?&nbsp;‘You could combine someone’s medical data, like blood research, with data about his or her dietary habits to see the effect of food intake on long term health. I live close to a chemical industrial site that leaks toxic waste from time to time. If you combine data from those leaks with the medical data of the local population, you could see the immediate results of a toxic waste incident on public health. Now it’s a manual process that takes seven to ten years, but data sharing and AI could shorten that to a few months. But we haven’t developed the ethical framework to orchestrate and safeguard data sharing yet.’&nbsp;&nbsp;How to build such a framework?&nbsp;‘That’s a good question. I’m thinking of a platform for data sharing that provides shared open standards and monitors the process. Something like the Supervisor authority in the telecom industry that safeguards against cartel agreements. You need both a legal and a technical framework that is data driven, so the whole process from&nbsp;utilization&nbsp;to application can be fully monitored.’&nbsp;&nbsp;Is it something you want to work on yourself?&nbsp;‘Personally I have decided to explore the whole field&nbsp;around data for some time. That’s why I have decided to leave NXP next year to work for&nbsp;Splunk, a data platform that wants to transform the fields around data, cybersecurity, IT and&nbsp;AI. My ambition is to eventually have a leading role so I can have a chance to do some good in the data driven world.’&nbsp;&nbsp;Can you be more specific?&nbsp;‘Data is the new resource, the new currency. It’s important to know what it can do and how you deal with&nbsp;that. A company has a social and&nbsp;ethical responsibility about the products they&nbsp;launch and as a leader you share that responsibility. If you cannot resolve ethical issues in a technical way, than perhaps you can do it contractually. If another company wants to use that smart&nbsp;microwave&nbsp;as a weapon, then perhaps you shouldn’t do business with them.’&nbsp;<hr>AI Leadership ForumSebastiaan is one of the keynote speakers during the first edition of the AI Leadership Forum on April 22nd. See the list of speakers and register&nbsp;<a href="https://hightechcampus.com/events/ai-leadership-forum" rel="noopener noreferrer" target="_blank">here</a>.High Tech Campus EindhovenArtificial Intelligence (AI) is a game changer both for industry and society. To ride the wave of this technological revolution, High Tech Campus Eindhoven in cooperation with NXP, Signify, ASML and Philips has recently opened the AI Innovation Center on Campus. In this 1100 square meter hub organisations can develop strategies, talent, knowledge and skills in the vastly evolving field of AI.&nbsp;In this interview series managers from the four founding companies share their insights on the role of AI both in their companies and the world at large.

Sebastiaan Laurijsse at High Tech Campus Eindhoven

Sebastiaan Laurijsse, Chief Transformation Strategist at Splunk and initiator of the AI Leadership Forum explains about the future of data as part of a series from the founding partners of the AI Innovation Center at High Tech Campus Eindhoven.

Data is the new resource and the new currency

Data leakage happens when the data an engineer used during training time becomes unavailable at the inference time. It can lead to over-optimistic predicted error rates because the data unavailable during inference time is considered predictive while undergoing the training process. The estimated error rates can misguide data scientists into implementing models that fail to meet acceptable accuracy thresholds. As a result, such models explicitly fail to perform accurate runtime predictions. Thus, engineers should put systems in place to monitor and debug the deployed models. If the models are giving predictions, they may fail to identify any data leakages in machine learning models.&nbsp;<h1 dir="ltr">Data Leakage: What Does It Have To Do With Machine Learning?</h1>Data leakage and machine learning are quite inter-linked. When engineers train a machine learning model, they aim for a model that scores high in specific metrics i.e. accuracy. Naturally, if they train a model and it scores well on a test data-set, they finalize the well-performing model.However, the engineers often encounter situations when the model gives good results while testing, but it doesn't achieve a similar performance level during real-world usage. For example, suppose a model achieves 99% accuracy in testing but fails to get anywhere near after getting productized when it acts on actual data sets. Such disparity in test and real-world performance is linked to data leakage.<h1 dir="ltr">Understanding The Key Reasons Behind The Data Leakage?</h1>Checking the machine learning model if it is achieving performance, which seems too good to be true, is the first step to detect data leakage. Some reasons for the same are:<ul><li dir="ltr">Use of duplicate data sets: It is common in models to feed data-sets from real-world, noisy data. Data duplication occurs in data-sets containing many points of nearly identical or identical data.&nbsp;</li><li dir="ltr">Data Mismatch: A mismatch between training data and production data can also result in data leakage in machine learning models.</li><li dir="ltr">Temporary data in processing: Although engineers may not explicitly leak information, they will still experience data leakage due to the training and test data sets' dependencies. A typical example is temporal data sets where time is a crucial factor, like the time-series data.</li><li dir="ltr">Preprocessing Steps of ML: Most engineers make the mistake of leaking data during the pre-processing stage of machine learning. They should provide such transformations the knowledge of the training set only and not the testing set.</li><li dir="ltr">Leaky Predictors: Engineers sometimes fail to exclude the updated features after realizing the target value during feature engineering. Thus, they encounter data leakage because when they make new predictions with this model, the data is not available to the model.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1611782271094-CN4INKb.png" style="width: 788px;" class="fr-fic fr-dib"><div>Image courtesy of&nbsp;<a href="https://www.kaggle.com/dansbecker/data-leakage" rel="noopener noreferrer" target="_blank">Kaggle.</a></div></li></ul><h1 dir="ltr">How Can Engineers Minimize Data Leakage When Building Machine Learning Models?</h1>Data leakage tends to occur when the model unintentionally has access to data during model training, which it will not have in the real-world. Understand that there is no one-size-fits-all solution. Engineers should carry out a proactive analysis for the potentially predictive variables and continuously monitor their models' output distributions. They can analyze the ground-truth signal shortly after generating predictions. Thus, to minimize data leakage when building machine learning models, they must take the following steps.<ul><li dir="ltr">Avoid over-optimistic evaluations of the models: The engineers must analyze the data generating process and know these pointers:<ul><li dir="ltr">What data is getting generated?</li><li dir="ltr">Where is it coming from?</li><li dir="ltr">When is it generated?</li><li dir="ltr">And how is it generated? Including random fields in the model can prove detrimental as it becomes difficult to detect it until it's too late.&nbsp;</li></ul></li><li dir="ltr">Split the training data-set into validation and train sets. It will prevent overfitting (or having too optimistic model performance evaluation), and they can avoid deploying underperforming models.</li><li dir="ltr">Temporal cutoff means removing all the data before the event in question. It helps engineers focus on the time of learning for observation rather than the time of occurrence of the observation.</li><li dir="ltr">Adding random noise in the input data can smoothen the effects of leaky variables.</li><li dir="ltr">Remove a variable if they suspect it is leaky.</li><li dir="ltr">Use pipeline architectures heavily, which allow them to perform a series of data preparation steps within the cross-validation folds - for example, <a href="https://www.kaggle.com/dansbecker/pipelines" target="_blank">scikit-learn pipelines and R caret package</a>.</li><li dir="ltr">Hold an unseen validation data-set, which will act as a final check of the model before finalizing it. As a thumb rule, the engineers can split the training data-set into validation and train sets, and then, they can hold back the validation data-set.&nbsp;</li><li dir="ltr">After completing the modeling process and before finalization, they can evaluate their model on the validation data-set. It will help them determine if they have made overly optimistic assumptions, and there is data leakage in their machine learning model.</li><li dir="ltr">If the validation of the model is based on a train-test split, then it is prudent to exclude validation data from any form of the fitting, which includes fitting the pre-processing steps. For cross-validation, they can ensure that they perform preprocessing inside the pipeline.</li><li dir="ltr">Although it requires huge data engineering infrastructure investments to capture, store and query the data, engineers must train their models on historical data snapshots.</li></ul><div class="fr-img-space-wrap"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1611782701823-Data+splitting.png" style="width: 788px;" class="fr-fic fr-dib">Image courtesy of Conlan ScientificData leakage is a detrimental issue that can result in lost profits, suboptimal user experiences, and life-threatening situations! If you think the assumption is far-fetched, consider the example of machine learning models deployed in hospitals to predict patient outcomes based on medical conditions. Hence, it becomes imperative for data engineers to prevent data leakage before finalizing their machine learning models.&nbsp;</div><h1 dir="ltr">Sources &amp; Further Reading</h1>Understanding Pipelines, Learn Machine Learning series by Kaggle, <a href="https://www.kaggle.com/dansbecker/pipelines" target="_blank">https://www.kaggle.com/dansbecker/pipelines</a>Avoiding Data Leakage in Machine Learning, Stephen Nawara, PhD., <a href="https://conlanscientific.com/posts/category/blog/post/avoiding-data-leakage-machine-learning/" target="_blank">https://conlanscientific.com/posts/category/blog/post/avoiding-data-leakage-machine-learning/</a>Ask a Data Scientist: Data Leakage, <a href="https://insidebigdata.com/author/dangutierrez/" target="_blank">Daniel Gutierrez</a>, <a href="https://insidebigdata.com/2014/11/26/ask-data-scientist-data-leakage/" target="_blank">https://insidebigdata.com/2014/11/26/ask-data-scientist-data-leakage/</a>

The accuracy of predictive modeling depends on the sample data's quality, and a robust model learned from that data. Data leakage may occur when the test and training data are shared in a model, resulting in either poor generalization or over-estimating a machine learning model's performance.

How to Overcome Data Leakage in Machine Learning (ML)

How big data, advanced analytics, and AI are changing manufacturing.

Industry 4.0 Deep Dive. Part 2: Data-Driven Intelligent Production

Infrared spectroscopy is the benchmark method for detecting and analyzing organic compounds. But it requires complicated procedures and large, expensive instruments, making device miniaturization challenging and hindering its use for some industrial and medical applications and for data collection out in the field, such as for measuring pollutant concentrations. Furthermore, it is fundamentally limited by low sensitivities and therefore requires large sample amounts.However, scientists at EPFL&rsquo;s School of Engineering and at Australian National University (ANU) have developed a compact and sensitive nanophotonic system that can identify a molecule&rsquo;s absorption characteristics without using conventional spectrometry. The scientists have already used their system to detect polymers, pesticides and organic compounds. What&rsquo;s more, it is compatible with CMOS technology.Their system consists of an engineered surface covered with hundreds of tiny sensors called metapixels, which can generate a distinct bar code for every molecule that the surface comes into contact with. These bar codes can be massively analyzed and classified using advanced pattern recognition and sorting technology such as artificial neural networks. This research &ndash; which sits at the crossroads of physics, nanotechnology and big data &ndash; has been published in&nbsp;Science.Translating molecules into bar codes The chemical bonds in organic molecules each have a specific orientation and vibrational mode. It influences the way molecules absorb light, giving each one a unique &ldquo;signature.&rdquo; Infrared spectroscopy detects whether a given molecule is present in a sample by seeing if the sample absorbs light rays at the molecule&rsquo;s signature frequencies. However, such analyses require lab instruments with a hefty size and price tag.The pioneering system developed by the EPFL scientists is both highly sensitive and capable of being miniaturized; it uses nanostructures that can trap light on the nanoscale and thereby provide very high detection levels for samples on the surface. &ldquo;The molecules we want to detect are nanometric in scale, so bridging this size gap is an essential step,&rdquo; says Hatice Altug, head of EPFL&rsquo;s&nbsp;<a href="https://bios.epfl.ch/" target="_blank">BioNanoPhotonic Systems Laboratory</a> and a coauthor of the study.The system&rsquo;s nanostructures are grouped into what are called metapixels so that each one resonates at a different frequency. When a molecule comes into contact with the surface, the way the molecule absorbs light changes the behavior of all the metapixels it touches.&ldquo;Importantly, the metapixels are arranged in such a way that different vibrational frequencies are mapped to different areas on the surface,&rdquo; says Andreas Tittl, lead author of the study.This creates a pixelated map of light absorption that can be translated into a molecular bar code &ndash; all without using a spectrometer.&ldquo;Thanks to our sensors&rsquo; unique optical properties, we can generate bar codes even with broadband light sources and detectors,&rdquo; says Aleksandrs Leitis, a coauthor of the study.There are a number of potential applications for this new system. &ldquo;For instance, it could be used to make portable medical testing devices that generate bar codes for each of the biomarkers found in a blood sample,&rdquo; says Dragomir Neshev, another coauthor of the study.Artificial intelligence could be used in conjunction with this new technology to create and process a whole library of molecular bar codes for compounds ranging from protein and DNA to pesticides and polymers. That would give researchers a new tool for quickly and accurately spotting miniscule amounts of compounds present in complex samples.

A new system developed at EPFL can detect and analyze molecules with very high precision and without needing bulky equipment. It opens the door to large-scale, image-based detection of materials aided by artificial intelligence. The research has been published in Science.

Infrared spectroscopy is the benchmark method for detecting and analyzing organic compounds.

A nanotech sensor turns molecular fingerprints into bar codes

Shimon, a robot in the Center of Music Technology and School of Music.

Compositions created using database of well-known pop, classical, and jazz artists

Robot Uses Deep Learning and Big Data to Write and Play Its Own Music

<h1>Executive Summary</h1><h2>Intro</h2>Wevolver is a platform &amp; community providing engineers informative content to help them innovate. Wevolver reaches millions of engineers every month in a very specific demographic: over 41% of them are between 25 and 34 years old, and less than 10% are older than 44.&nbsp;Geographically, European countries represent 28% percent of the audience, the USA 26%, and beyond India&#39;s 10%, the audience is spread widely across the globe.This provides a unique opportunity to learn about this engineering generation&#39;s interests, concerns, and points of view. Previously other interesting reports have been published by engineering organizations: For example, Hackster.io reported in 2016 on the maker community, Fictiv holds a yearly survey among mechanical engineers (primarily in the US), and IEEE conducts their &#39;Pulse of Engineering&#39; report. We think that Wevolver&#39;s &#39;Engineering State of Mind&#39; research complements those because of the specific demographics of its audience, as well as the report&#39;s specific focus on how this cohort views the world.(The report is also available as a download. Click <a href="http://bit.ly/33dSx64" rel="noopener noreferrer" target="_blank">here</a> to access the PDF)<h2>About the People</h2><ul type="disc"><li>+200 people respondents who were notified through Wevolver&#39;s newsletter and social media posts and responded between July 15th and August 31st, 2019.</li><li>Represent a wide range of industries.</li><li>60% were professional engineers, working at a wide range of company sizes; from sole proprietors to +100.000 people.</li><li>82% of students are at undergraduate level or higher.</li></ul><h2>Main Outcomes</h2>1. Engineers are extremely worried about climate change. 2. Engineers think A.I. is strongly under-appreciated. 3. Engineers are strongly interested in the field of robotics.<hr><h1>All Results</h1><h2>Demographics</h2>Now let&#39;s dive deeper into the results of the survey. Starting with the demographics:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571995166383-Slide4.JPG" style="width: 1114px;" class="fr-fic fr-dib"><img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571995206862-Slide5.JPG" style="width: 1097px;" class="fr-fic fr-dib">Only 1% of survey respondents label themselves as a Hobbyist Maker. At the same time, we know from conversations we&#39;ve had that many professionals do perform &#39;Maker&#39; activities in their spare time: Examples range from developing electric motorbikes to tinkering with 3D printing.Among the professionals the majority has a role in engineering; beyond the most represented roles such as Mechanical Engineer, Electrical engineer, and Software engineer, there is a whole range of disciplines; Chemical Engineer, Civil Engineer, Biomedical Engineer, etc. There is also a significant amount with a pure leadership role, many of which who have been trained as an engineer, and 9.9% works as a researcher. Other roles that were expressed include education, analyst, and helicopter pilot. Among students disciplines include Mechanical and Electrical Engineering, Robotics, Aerospace, and Computer Science.These roles are applied in a wide range of industries, as we&#39;ll see next:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571995345594-Slide6.JPG" style="width: 1102px;" class="fr-fic fr-dib">Sixteen percent of respondents are active in the Industrial Robotics &amp; Automation industry (interestingly, as you&rsquo;ll see later, a much larger group (over 66%) of all respondents actually lists robotics as the topic they are most interested in).&nbsp;Services, ranging from manufacturing to engineering and design services, are also strongly represented, and software is a third runner up. Beyond those, our respondents&rsquo; industries cover a wide range. Other industries mentioned include VR, Art &amp; Design, Chemical, Naval, and Aeronautics.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571995265581-Slide7.JPG" style="width: 1114px;" class="fr-fic fr-dib">It appears that engineers have a voice: The majority is involved in day to day engineering decisions, and many are involved in determining the direction of their company&#39;s R&amp;D. When we zoom in, we see that the percentage of engineers being involved in setting R&amp;D directions is significantly higher at small and medium size companies (64% versus 44% at companies over 500 employees).Engineers are involved in purchase decisions, especially when we zoom in on smaller companies. While the total percentage of being either involved in or making purchase decisions is roughly equal for all types of companies (around 47%), when looking at small companies the amount of people who actually make purchase decisions themselves (17%) is more than double that of large companies.<h2>Insights</h2>We could label this cohort as relatively young (with 91% being below 44), well educated, and representing quite a wide range of industries and company types. These people work in the most innovative companies in the world and are responsible for developing the products and technologies we&#39;ll use. Furthermore, the influence of this cohort is likely to increase as they progress in their careers and continue to grow their skills, experience, and knowledge.Therefore, we think the perspective of this community matters and that&rsquo;s what we&rsquo;ll dive into now.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571995456957-Slide8.JPG" style="width: 1114px;" class="fr-fic fr-dib">We inquired which topics these engineers are most interested in to learn about. The results from the survey correlate with what we see are the most popular content topics on the Wevolver platform.Robotics &amp; Automation dwarf other topics of interest. This is interesting as only 16% of respondents are actually active in the industrial robotics and automation industry. Clean Energy is a good runner up which is not surprising given the concerns engineers have about climate change and environmental issues. In that light, Clean Energy could have scored even higher. One explanation might be that engineers think sustainability challenges need to be tackled on many facets, as we&#39;ll see later in the survey (for example, one respondent commented how advances in quantum computing could help solve complex environmental issues). The strong interest in A.I. and other computing advancements (including big data and quantum computing) is in line with the rapid developments in this field, as well as with the point of view from engineers that A.I. as a technology is strongly under-appreciated. Worth noting is that A.I. forms an essential component of the topic of robotics as well as of autonomous vehicles (autonomous vehicles sits among the top 3 topics for over 21% of the survey respondents as well). Respondents also mentioned their interest in the ethics involved in A.I. We consider the relatively low interest in AR/VR surprising compared to the size of the interest (or hype) in media and venture capital. &nbsp;Engineers have many interests, and other topics mentioned include medical technology, astrophysics, and; &quot;literally everything.&quot;<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571995505356-Slide10.JPG" style="width: 1114px;" class="fr-fic fr-dib">Given the sizable interest in Robotics we zoomed in on the people interested in this topic and explore which subdomain they are focused on. In this instance we enquired specifically which fields engineers wanted to grow expertise in. Our view is that this question reveals being active or an interest to become active in the chosen domain, beyond being purely interested an sich. This gives an indication of the importance engineers deem the domain to have in the future.Both Collaborative Robots (&#39;cobots&#39;) and Human-Robot Interaction are the big standouts here. These two topics also overlap and are related: Collaborate Robots are defined as &ldquo;robots intended to interact with humans in a shared space or to work safely in close proximity. Cobots stand in contrast to traditional industrial robots, which are designed to work autonomously with safety assured by isolation from human contact.Human-Robot Interaction (HRI) describes the studying or designing of robotic systems for use by or with humans. Developing collaborative robots requires designing how humans engage with these machines and through which interfaces. Apart from its application to collaborative robots, HRI applies to many (if not all) fields in robotics, especially with the growing cognitive capabilities of robots this field has seen significant growth.Cobots and HRI stand out, which can be expected given the growth and size of both fields. In our eyes, the most remarkable result is the significant interest in the domain of bio robotics. At the moment this field is tiny from a commercial perspective but apparently expected to grow in importance.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571995563949-Slide12.JPG" style="width: 1114px;" class="fr-fic fr-dib">This question reflects the impact engineers think a given technology will make in comparison to the attention the technology receives from society. Here engineers point to a clear conclusion: &quot;A.I. &nbsp;will be huge, and society doesn&#39;t see how big its impact will be.&quot; We found this result striking because, at first glance, there doesn&#39;t seem to be a lack of attention to A.I. in media and within companies. However, engineers think the impact of A.I. will be orders of magnitudes bigger than reflected by that amount of attention. Secondly, people we spoke with indicated that their perspective is most of us don&rsquo;t conceive how much of our lives and society will change because of implementations of Artificial Intelligent systems&nbsp;<blockquote><blockquote><blockquote><blockquote>Engineers point out that the implications and impact of A.I. are significantly bigger than what people generally expect.</blockquote></blockquote></blockquote></blockquote>Up to this point, the survey uncovered unexpecting insights. On top of that, the one that completely stopped us in our tracks was the absolutely unanimous answer to our question about challenges the world faces:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1571995582131-Slide13.JPG" style="width: 1114px;" class="fr-fic fr-dib">Take note: The smartest minds on our planet are deeply worried about the health of our environment. This topic overshadows seemingly high impact challenges as poverty and healthcare, and even the potential negative aspects of A.I. are seen as a significantly impactful challenge. As one said, &quot;Without a healthy habitable planet, nothing else matters.&quot;Respondents had to answer this question in an open-ended way. After the survey, we classified all responses. Climate change, environment, and clean energy were so often mentioned in a single answer and used interchangeably that we decided to classify them under a single banner. Note that the survey was held over the summer, before the current global climate crisis strikes and relatively heightened attention to the topic.We had expected a wider distribution of challenges, and for example expected a higher attribution to robotization and employment. At the same time, high concerns about our environment correspond with the activity we see happening in Wevolver&#39;s community: discussion threads about the topic consistently generate a high level of engagement, and any knowledge share on the topic is likely to spark a high amount of interactions.<blockquote><blockquote><blockquote><blockquote>&quot;Without a healthy habitable planet, nothing else matters.&quot;</blockquote></blockquote></blockquote></blockquote> <h1>Conclusion</h1>We perceive this cohort of engineers being relatively strongly socially engaged. Socio-economic issues and the ability to contribute matter to them. This is reflected in the results of our inquiries into these engineers&#39; concerns, interests, and world view. You cloud also label them as relatively &#39;techno-optimistic:&#39; Many think technological advancements can solve current challenges, or at least need to play an essential role in solutions.Engineers ride an interesting balance. Generally optimistic about the role that technology plays, they also are more clearly aware of its current state and limitations. &nbsp;For us, the research&#39;s insight into engineers&#39; concern about our environment has been the most striking and we think it&rsquo;s a telltale sign. Based on this research, we have increased the informative content we share about this topic, as well have started more discussion threads and Q&amp;A sessions to grow the available knowledge and engagement.A.I. and robotics are the two big other topics that engineers pay attention to. According to engineers, our society underappreciates the impact of A.I. Besides this, we think that the research also points to a discrepancy between the interest of engineers in robotics, and the level of attention the subject gets from the general public, media, and politicians. We recommend leaders to take note of this, and increasingly opening up the conversation with the people who are developing today&#39;s and tomorrow&#39;s technologies. Ultimately, engineers are the ones who can most clearly see both capabilities and limitations of technology, as well as its future directions.&nbsp;We&#39;d like to thank all survey participants, as well as our community members who helped spread the survey.&nbsp; Please share your feedback and questions, we&#39;re looking forward to hearing from you. &nbsp;On behalf of team Wevolver; Bram Geenen,&nbsp; CEO Wevolver.(The report is also available as a download. Click <a href="http://bit.ly/33dSx64" rel="noopener noreferrer" target="_blank">here</a> to access the PDF)<hr><h2>About Wevolver</h2>Wevolver is a platform &amp; community providing engineers informative content to help them innovate. Wevolver reaches millions of engineers with articles, project descriptions, videos, and engaging conversations about state of the art technology that enable to keep up to date and find knowledge when engineering. The information on Wevolver&#39;s platform is created by a community of engineers, Wevolver&#39;s network of academic researchers, and editorial staff. Companies can sponsor content on the platform. For this work Wevolver won the SXSW Innovation Award, the Accenture Innovation Award, and the Top Most Innovative Web Platforms by Fast Company. Wevolver is how today&#39;s engineers stay cutting edge.

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