Novel Effect elevates storytelling with tools that let creators add immersive effects and interactivity to traditional media—books, television, film—and a powerful speech-recognition engine to enable that content for audiences across a wide range of platforms and devices

Novel Effect

Voice technology is taking many industries by storm, and the publishing industry should be no exception. Yet, in an industry built upon dedication to thorough immersive reading, it’s easy to understand why new technology is not always rapidly adopted. Still new technologies have proven to help grow revenue for publishers and authors alike. One current example is Audiobooks, are widely-considered to be the most quickly growing niche in the publishing world — . This year’s figures confirm the trend. According to , ‘Downloaded Audio’ is still the fastest growing format in publishing — up 36,5% for the first two months of 2019 as compared to the same period in 2018. 2017 alone saw $2.5 Billion in audiobook sales the AAP StatShot Monthly report Voice technology has seen huge advancement in the past decade. The number of people who utilize such technology is growing considerably; many of us rely on smart tools like Apple’s Siri or Amazon’s Alexa to prompt our devices to perform quick, handy tasks for us without having to push a button. There’s no secret about it: more and more of us are caving to the tantalizing prospect of smart home devices and ultra-advanced tech. are anticipated to install smart home devices by 2022 — and smart speakers will likely account for nearly 70% of that figure. When publishers step back and realize that each of these devices presents an opportunity for exposure and sales, the situation looks exceptionally interesting. Over 240 million US homes Yet, voice tech has failed to serve the publishing industry as well as it should have so far. Top Voice Assistants Recognize Only 43% of Basic Queries Related to New York Times Bestsellers If you are a publisher, are you taking advantage of everything voice technology has to offer for you and your authors? performed an experiment utilizing the top 15 fiction and non-fiction titles from The New York Times bestseller list. They queried a number of popular voice assistants like Alexa and Siri for basic information and tasks — they asked for the title of each book, stated that they wanted to listen to each book, etc. The results were startling: the top voice search tools can only recognize approximately 43% of the key searches for best-selling titles, including, “I want to read X book by Y author,” and “I want to listen to X book by Y author.” One WNIP contributor With already in American homes and businesses, many readers are using their voice assistants to locate and purchase book titles –and if your titles aren’t recognized by those platforms, you’re missing out on the opportunity to connect with those readers. In many cases, that may mean you’re losing a significant percentage of the sales you could bring in if they were readily available. You want people to be able to find your titles, and that doesn’t only refer to bestsellers, but backlist titles and audiobooks as well. 66 million devices How Publishers Benefit from Voice Technology Some companies have moved to leverage voice technology in ways that allow them to drive sales and introduce consumers to titles they wouldn’t have purchased otherwise. For instance, , known as a “Stephen King Library,” designed to quiz readers on their preferences before offering them a suggestion of which Stephen King title to pick up next. Simon & Schuster released their own Alexa skill Certain publishers have taken steps to get even more creative with their voice tech offerings. Novel Effect, a company in cahoots with the Alexa Accelerator program, designed to complement in-person live readings with a variety of audio soundscapes. This technology can benefit educational publishers as it not only does this technology bring listeners into the fold of the story they’re hearing or the information they’re learning, but it’s been shown to increase reading retention and comprehension in classrooms. The soundscapes can also produce greater engagement for trade publishers who need to contend with the fact that younger consumers crave more than simple text based experiences on their mobile devices. launched an Alexa skill Audio games are becoming part of the package. Westworld, for example, already has its own audio game, which interested fans can play through Alexa. These games can help increase hype and encourage interest in new stories and familiar ones alike — and they’re a great technique for adding to the content offered along with your best sellers. Now is the Time to Take Ownership Voice technology is growing fast, but many publishing companies haven’t yet of the facets they’re able to control. Now is the perfect time to take advantage of that, creating content that will intrigue readers and encourage them to come check out your books. taken ownership Fortunately, there are a number of steps that publishers can take to increase their chances of success and take matters into their own hands. The beginning of the process could be as simple as performing independent audits of various voice assistants and addressing issues with tech companies, but more motivated teams have the world at their fingertips. This approach can begin simply and become more granular and targeted as time goes on. Making sure all titles have the basic metadata makes for an excellent starting point. Once issues have been pinpointed, it’s the publisher’s responsibility to open up communication with voice teams from whichever tech company’s voice assistant is causing trouble. Publishers can report problems, request changes, and receive guidance on how to move forward with fixing things publisher-side. Down the road, more advanced metadata like keywords and awards can be added. Audit your catalog’s voice-first compliance Create an app that will allow readers to search for “books like [your current best-seller or new release].” Provide lists based on popular searches and choices. Offer title recommendations based on previous titles read “Who wrote [X]?” “What books did [Y] author write?” Many people use voice recognition devices to get fast answers to their questions. By ensuring that they are part of the program, you increase the odds that your books and authors will be recognized by the voice search platform. Create the answers to common questions and make them part of voice-recognition programs You likely already offer audiobooks on these platforms. In order to take full advantage of voice technology, however, you need to go beyond mere audiobooks. Consider how an auditory component could add to the reading experience, whether that means you’re providing a soundtrack while your readers enjoy a physical book or offering additional content that is only available via the device. Deepen the reading experience By integrating your apps with voice-assisted devices, you can make your books part of the readers’ regular routines. Do your readers start their mornings with an inspirational or spiritual reading? Do they want to check out the news or have a quote of the day delivered to them while they enjoy their coffee? As you become part of your readers’ routines, you also increase the odds that they will want to continue to purchase your books and materials in the future — especially if you issue recommendations for the next title as they near the end of a book. Connect with your readers’ routines There are several key strategies you can use to transform your marketing efforts, including transforming your search keywords to meet those needs. Keep in mind:- Voice searches are often longer than text searches.- It’s important to plan for questions asked with a natural sentence structure in place.- Optimizing for Google’s featured snippets is a great way to get your content noticed. Optimize your ‘voice’ marketing strategies If publishers take ownership of voice apps and ideas now, they can get ahead of the curve of increasing sales through new technology, rather than having to play catch up down the road. Your publishing company will be part of the standard programming on those devices — and your readers will be more likely to turn directly to you to discover that information, too. Conclusion It’s easy to consider major technology improvements to be the domain of the large tech giants, but the fact of the matter is that there is much a publisher can do today to reap the benefits of voice tech. The process may not be simple, but a little forward thinking and innovation will go a long way. If you find the prospect of integrating voice tech into your business models a little daunting, find a trusted technology partner that will walk you through the process. Whether it’s an Alexa skill that offers suggestions or an app that enhances the audio experience as it happens, voice technology brings in countless new and incredible capabilities for the publishing industry, serving to boost readers’ interest and enhance their time spent devoted to literature. By ,VP, Media & Entertainment at DataArt Max Kalmykov

Why Modern Publishers Cannot Afford to Ignore Voice Technology

Yes, we’re witnessing a crisis unfolding. Not only people get gripped by the coronavirus fear. It’s like a stroke for most businesses — some will recover, some won’t. Does this corona crisis necessarily mean a failure for your business? If you play your cards right, Covid-19 could spell… success. So, no need to read coffee grounds to tell what awaits your business, experts advise on how to make the most of the current situation. Now let’s have a closer look at the tricky marketing situation that industries face right now. People stay at home People are staying at home. There’s no telling what damage coronavirus will bring for travel services. It could take has sent it to. So, many of them will have to lie low unless they find a way to adjust. months for the industry to recover from the tailspin the coronavirus Another wave of impact runs through e-learning: Covid-19 compelled the . In response, education technology companies have stepped forward. Mass . shift to virtual learning technology adoption is what drives the dynamics of ed-tech financing People still need to eat. in the order number. To minimize the danger while passing the packages, some delivery apps now provide the option to ask for a no-contact drop-off. Food-delivery expects a high surge People adjust to working from home. Quarantine pushed . Workplace software companies do their best to stand up to extraordinary demand for facilities for corporate communication, virtual meetings, and training sessions. WFH from a growing trend to a central focus Growing industries Amid coronavirus pandemic, . Coronavirus increases users’ reliance on it. For example, China has moved half of its medical care online. This dramatic increase in virtual visits slowed down coronavirus transmission. Virtual services, like online symptom-checking tools and remote consults with doctors, could keep the so-called “worried well” from flooding hospitals. telemedicine is revolutionizing healthcare People stockpile. The growing alarm over the coronavirus makes them . Some experts claim that this will have a permanent effect on future shopping habits. The analysis shown an 8.8% increase in conversion rates, compared with the same period the previous year. place large orders More and more . Due to the coronavirus, retailers supplying like toilet paper, face masks, and water bottles are having significant sagging under the weight of surge in demand. Still, in the long-term, these retailers are concerned the coronavirus will negatively impact their revenue in 2020. buyers who were visiting websites do make a purchase Startup rescue plan That said, Covid-19 is likely to fuel areas like online . shopping, e-learning, healthcare, delivery service, and remote work Clearly, it’s no longer business as usual, no longer a regular market, especially if you’re leading a startup or a small company. Given different degrees of preparedness across companies, we’ve prepared a general : action plan to tackle this pressing challenge and test your assumptions about customers and revenue. You need to figure out your actual burn rate and runway in this new environment now. Estimate the risks Set up a , set up communication standards and tools to stay productive. Remember that you are . WFH environment recognized for what you do, not your time in the office chair from the team and maintain a healthy team spirit. Your team can be just as happy, connected, and productive at home as they are in the office. Explain your expectations you have on hand and picture how you could make it last for at least six to nine months. And if you don’t have enough, look at how you could cut expenses or increase sales by doing something different. Analyze the resources with a better value for money ratio. Find alternative development resources Switching the non-vital process to an outsourced team can greatly decrease costs.  Besides, unlike in-house employees who may not be able to find a replacement if they get sick, you can rest assured the job is covered with no gap by an outsourced team. than ever. Come out of this disruption stronger Of course, coronavirus isn’t something we’d have wished. But from scarcity and reframed perspectives comes innovation and creativity. that could quickly produce a lot of ventilators, masks or gowns, screening existing drugs for effectiveness, novel approaches to vaccines, and new therapeutics that big pharma companies are unlikely to develop. Investors are seeking startups , so . For instance, a health services startup and a provider of telemedicine, which previously specialized in Opioid Use Disorder treatment, managed to meet the growing demand for its virtual medication-assisted treatment programs due to Covid-19. As a result, Series A capital fundraising was announced. Its online portal quickly collaborates with on-site providers and transition patients into virtual care at home and already works in remote and rural areas. Startups are flexible adjust to changes faster This example shows that a startup leader needs to plan, communicate, and act. Together, we can get through this. https://www.zfort.com/blog/coronavirus-pandemic-guide-to-survive-startups

Surviving Coronavirus Pandemic: A Guide for Startups

The Reward Function by Midjourney. Generative transformer-based models have received a lot of attention since the introduction of ChatGPT with its remarkable Large Language Model (LLM) capabilities. But another, less remarked artificial intelligence technique keeps peeking from behind the curtains: Reinforcement Learning, which is being used to tame unruly generative AI with a carrot-and-stick approach. Reinforcement Learning (RL) is finally getting more attention as the big names in AI like Google, Deepmind, and OpenAI are publishing more papers on its use to solve problems that LLMs cannot reach. Nature published a paper this year from Deepmind that used reinforcement learning to code a more efficient implementation of the C++ sort() library function. RL algorithms were developed by Richard Sutton and Andrew Barto based on operant conditioning in behavioral psychology as described by B.F. Skinner and Ivan Pavlov. But it has languished in the background of artificial intelligence research - Turing Award winner Yann LeCun famously likened it to the cherry on top of the artificial intelligent cake (with supervised learning as the icing and unsupervised learning as the cake itself. RL is a type of machine learning where the AI system, called an agent, learns through trial and error with its environment. It's how humans learn many skills - by trying different things and seeing what works - and how we learn what to avoid, like the pain of being stung by an insect. The agent gets feedback on its actions as rewards (punishments are negative rewards), and its goal is to collect as much reward as possible over time. This makes reinforcement learning a great fit for iterative decision-making problems. Compared to supervised learning, where the AI is trained on labeled datasets of correct answers, and unsupervised learning, where the AI finds patterns in unlabeled data, reinforcement learning doesn't need huge datasets. The agent simply learns from its own experience interacting with the environment, like children discovering how the world works. Reinforcement learning also differs from genetic algorithms, where the fittest agents are selected over generations - in reinforcement learning, each agent learns within its lifetime. Certain qualities make a problem a good fit for reinforcement learning. The problem should have a quantifiable reward function that provides clear feedback on how well the agent's actions accomplish the intended goal. For example, a maze navigation task would score the agent higher for moving closer to the exit. Without this measure of progress, the agent cannot determine which behaviors are effective. Second, the problem should allow the agent to continually take actions and learn from the results. If the agent must make all its decisions simultaneously, it will not be able to adjust based on experience. Instead, reinforcement learning works best in iterative environments where the agent can try different approaches over time. Finally, the problem must permit mistakes during the trial-and-error learning process. Reinforcement learning fundamentally involves exploring through less optimal actions. If errors are acceptable while the agent learns, reinforcement learning can discover novel and effective strategies through experimentation. Problems like robot control, game-playing, code-writing, automated unit testing, and automated trading satisfy these criteria. The agent can take actions, evaluate their impact quantitatively, and adapt its behavior over time. While tradeoffs exist, reinforcement learning opens solutions not possible with other machine learning approaches. By embracing the goal-seeking process, reinforcement learning expands the range of what AI can accomplish. Reinforcement learning has the potential to transform industries where human oversight is impractical or solutions aren't obvious. Dr Phil Winder, author of a recent book on Reinforcement Learning, curates case studies of its application (https://winder.ai/tags/case-study/) Drug Discovery Developing new medicines involves immense trial and error. Reinforcement learning can simulate this more efficiently by finding relationships in existing medical data. Since live human drug testing is unethical, reinforcement learning gives a faster alternative to traditional techniques. The algorithm explores links between chemical properties, genes, and disease interactions to pinpoint promising research directions for scientists. Automated Trading Reinforcement learning trading systems like RBC's Aiden make investments by recognizing patterns from market experience. Reinforcement learning suits the noisy, rapidly changing finance world where hand-coded models fail. Trading algorithms using reinforcement learning adapt based on what works rather than needing constant human updates. However, challenges remain in avoiding overfitting limited data and ensuring ethical agent behavior. Automated Unit Tests In the context of unit test code-writing, the environment is the space of potential unit test programs, and the agent is trying to find the best possible unit tests that maximize coverage of the code under test and find the most bugs. Reinforcement learning can probabilistically search the set of possible test methods for a given program, analyze the program, and then write tests autonomously. The trial-and-error approach means the test code generated has been run and therefore its behavior is known and can be evaluated. This approach allows entire suites of fully functioning unit test code – tens or hundreds of thousands of tests – to be written without human code review. Game AI Reinforcement learning enables more lifelike, human game AI. Instead of following scripted rules, reinforcement learning agents actively explore environments, discovering creative solutions a human may miss. Open-ended games like Minecraft are an exciting new frontier for reinforcement learning in games. So far, it shows promise for interesting, emergent AI behaviors. One major challenge with reinforcement learning agents is that they require large environments. With insufficient data, the agents will fail to generalize properly to new situations and perform poorly. Unfortunately, in many real-world scenarios, acquiring enough quality data is difficult and time-consuming. Without enough data, the agent's learned behaviors will be brittle and limited. Another hurdle is that real-world conditions are often shifting, not stationary. Changes in the environment may suddenly render an agent's previously learned behaviors obsolete or suboptimal. To deploy reinforcement learning successfully, agents must have techniques to quickly adapt their policies to respond to changing conditions. A third issue is balancing exploration against performance. Exploration through random actions is key to reinforcement learning, but excessive random acts will hurt short-term results. Agents must temper undirected exploration to focus on exploiting known rewards while still trying new approaches when needed. Striking the right balance remains an open challenge. Finally, without close human oversight, agents may learn harmful policies that exploit loopholes in the reward system. Researchers must develop methods to align agent objectives with human values and predict how learned policies will generalize. Reinforcement learning offers enormous potential, and research is progressing swiftly to address these challenges in areas like sim-to-real transfer - the process of training a reinforcement learning agent in a simulated environment and then transferring the learned policy to the real world - reward design, and interpretability. LLMs currently hog the limelight, but RL unlocks solutions where neural network techniques falter. As research tackles current weaknesses, expect to see growing use of reinforcement learning in medicine, finance, coding, robotics, and more.

Beyond Transformers: 
The Overlooked Potential of Reinforcement Learning

How does the human brain decide someone is beautiful? It is something universal or subjective? This AI attempts to help give us some answers to these questions. This AI reads your brain to generate personally attractive faces. It generates images containing optimal values for personal attractive features on a human face! Watch the video References DeepFakes in 5 minutes: GANs explained: An amazing application of GANs: Read the article: M. Spape, K. Davis, L. Kangassalo, N. Ravaja, Z. Sovijarvi-Spape and T. Ruotsalo, "Brain-computer interface for generating personally attractive images," in IEEE Transactions on Affective Computing, doi: 10.1109/TAFFC.2021.3059043. https://youtu.be/Upv0kMLx7xI https://youtu.be/ZnpZsiy_p2M https://youtu.be/zB_jQ8SUjKE https://whats-ai.medium.com/ Abstract— While we instantaneously recognize a face as attractive, it is much harder to explain what exactly defines personal attraction. This suggests that attraction depends on implicit processing of complex, culturally and individually defined features. Generative adversarial neural networks (GANs), which learn to mimic complex data distributions, can potentially model subjective preferences unconstrained by pre-defined model parameterization. Here, we present generative brain-computer interfaces (GBCI), coupling GANs with brain-computer interfaces. GBCI first presents a selection of images and captures personalized attractiveness reactions toward the images via electroencephalography. These reactions are then used to control a GAN model, finding a representation that matches the features constituting an attractive image for an individual. We conducted an experiment (N=30) to validate GBCI using a face-generating GAN and producing images that are hypothesized to be individually attractive. In double-blind evaluation of the GBCI-produced images against matched controls, we found GBCI yielded highly accurate results. Thus, the use of EEG responses to control a GAN presents a valid tool for interactive information-generation. Furthermore, the GBCI-derived images visually replicated known effects from social neuroscience, suggesting that the individually responsive, generative nature of GBCI provides a powerful, new tool in mapping individual differences and visualizing cognitive-affective processing. Video Transcript: 00:00 i think you will all agree that when you 00:02 look at someone 00:03 and find the person attractive you 00:05 cannot explain why 00:07 there are many reasons involved in this 00:09 decision 00:10 plus some of these reasons may not even 00:12 be directly related to the physique of 00:14 the person 00:15 still when you look at a picture of 00:17 someone you instantly know if the person 00:19 is attractive or not by your standards 00:21 and you know this without any doubts but 00:24 how does that happen 00:26 can we explain or understand what beauty 00:28 is to us and worse can someone else 00:31 understand what we find beautiful is 00:34 beauty even something universal 00:36 or subjective to me it's definitely 00:39 something subjective 00:40 just like art as we say beauty is in the 00:43 eye of the beholder 00:44 isn't it but then why specific people 00:47 become 00:48 actors or models and not others is there 00:51 a universal beauty that we could explain 00:53 is beauty something cultural biological 00:56 or a blend of both 00:58 i think these are mostly philosophical 01:00 debates that we can discuss in the 01:01 comments 01:02 for those of you that will like to i 01:04 would love to chat and even debate about 01:06 this with you 01:07 anyways back to what we know about 01:09 beauty as we know 01:10 humans typically respond emotionally to 01:13 attractive images 01:14 rather than purely based on rational 01:16 visually salient reasons 01:18 which is what makes beauty so subjective 01:20 but is there a way to understand what we 01:22 think 01:23 of beauty and use this information to 01:25 create attractive pictures 01:27 can we understand our brain signals to 01:29 achieve that 01:30 and is it even readable to these signals 01:33 well 01:34 this is what michel spape and his team 01:36 from the university of helsinki 01:38 attempted to do what you see here is 01:41 called electroencephalography 01:43 or eeg it's a monitoring method used to 01:46 record electrical brain activity 01:48 using electrodes placed along the scalp 01:51 specifically they tracked 01:52 two different channels with these 01:54 electrodes which are known to be 01:56 responsive to stimuli that are either 01:58 novel or effectively evocative or 02:01 mentally demanding and related to 02:03 working memory updating 02:05 meaning that the stimuli are improbable 02:07 and require a mental or physical 02:09 response 02:10 but they employed two strategies in 02:11 their experiments 02:13 first they made the supposed attractive 02:15 images relatively improbable to appear 02:18 they also asked the participants to 02:20 focus on attractive faces 02:21 by mentally counting their occurrence to 02:24 me these strategies seem like a 02:26 considerable bias to detecting 02:28 actual attractiveness where the signal 02:30 responses mainly tell us two things 02:33 one that the person is lightly surprised 02:35 because the attractive pictures are rare 02:37 and two that the person is mentally 02:39 active trying to remember the count and 02:41 add to it 02:42 i personally see it as an overkill way 02:45 of asking whether the person finds the 02:46 face attractive or not 02:48 since it does not really measure the 02:50 beauty of the image scene 02:52 and here i am not entering about the 02:54 details of the data set they used to 02:56 train their model 02:57 which is only made of celebrities 02:59 nonetheless 03:00 this was undoubtedly a great way to find 03:03 out 03:03 which faces were attractive or not to 03:06 train their model to focus on beautiful 03:08 faces 03:08 which is a very cool application of guns 03:11 as you can see it clearly worked out 03:13 there's a significant difference between 03:15 responses after a slight delay of time 03:17 starting approximately a fourth of a 03:19 second after showing the image 03:21 where the pink lines are the two channel 03:24 responses i just mentioned 03:25 are shown here the top shows the frontal 03:28 response 03:29 responsible for the novel and 03:31 effectively evocative stimuli while the 03:33 bottom is the parietal response 03:35 responsible for mentally demanding 03:37 stimuli here 03:38 the faces deemed attractive are green 03:40 unattractive in red 03:42 and inconsistent responses in gray these 03:45 inconsistent responses signified that 03:47 the data 03:48 was not used due to its low level of 03:50 confidence 03:51 attractiveness was confident when both 03:53 electrodes evoked more positivity for 03:55 the attractive faces 03:57 now let's see how they created such a 03:59 model to use their brain activity to 04:01 generate attractive faces 04:03 at first they needed to generate random 04:05 images 04:06 to show the test subjects to do that 04:08 they used a gun model trained on 200 000 04:11 images of celebrity faces already 04:14 introducing a bias 04:15 but still let's keep going they then had 04:18 a latent space 04:19 from which they could use to generate 04:21 new artificial images 04:22 this is just like any other gun model 04:25 like deep fakes 04:26 it is very similar to what i already 04:28 explained in previous videos 04:29 and you are most certainly aware of how 04:31 it works so i won't cover it again here 04:34 but if you are not 04:35 you are free to click on the top right 04:37 pop-up and watch my explanations of 04:39 gan architectures and i'll talk a little 04:41 bit more about this latent space 04:43 later on then each participant was asked 04:47 to go through 04:47 randomly generated images remember here 04:51 they had to count when they see an 04:52 attractive face mentally 04:54 once the results are all compiled they 04:56 train a classifier on these brain output 04:59 signals 04:59 containing the image's subject 05:01 assessments 05:02 basically ideally containing whether the 05:05 face was attractive or not 05:07 once the classifier is trained it's used 05:09 under brain electrical responses 05:11 on randomly generated images note that 05:14 all these randomly generated images come 05:17 from a latent space originally 05:19 created by the encoder of the gun 05:21 trained on the first step 05:22 as you can see here the latent space is 05:25 a space where one point represents 05:27 an input the generator can use to create 05:29 a unique image 05:30 this space contains all the faces that 05:33 this generator can make 05:35 thus when an image is generated using a 05:37 point in the space 05:38 or feature vector it can be considered 05:41 attractive or not 05:42 by the classifier if that's the case it 05:44 will encourage the network to create 05:46 images by selecting the generator's 05:48 input close to this point in the space 05:50 focusing the generator's attention on a 05:52 subspace 05:53 where the features of the generated 05:55 faces are pleasing to the participant 05:58 finally this is how new images 06:00 containing optimal values for personal 06:02 attractive features on a human face 06:05 are generated i hope you enjoyed this 06:07 video 06:08 and i definitely invite you to read 06:09 their paper it's very interesting and 06:12 well written it's the first link in the 06:14 description don't forget to like and 06:15 subscribe if you enjoyed the video 06:17 and share your thoughts in the comments 06:19 where i will answer you in the following 06:21 7 minutes 06:22 i promise thank you for watching

Is Subjective Beauty Something We Can Model with AI?

What are the key leadership qualities that inspire teams to build amazing products? is a difficult and complex job — but also fascinating. It requires a bold vision and, at the same time, a strong connection with reality: successful Product Managers are both and — they are and have the ability to see when others are ‘lost’ in ambiguity. Building great products visionaries pragmatists strategic thinkers product opportunities In fact, I prefer using ‘leadership’ over ‘management’ in the context of product development: True Product Leaders don’t just a product; they envision great products and features out of the blue; they spot those between a superior product and an average one; they know how to build a product from scratch — how to handle ambiguity and shape the scope; where to and how to features in a smart and strategic way. manage critical differences focus prioritize Talented Product Leaders make epic things happen — by releasing the , in the right order and at the right time. They act as the ‘CEO of the product’ — they bring a bold vision, the strategy, and the ambition to deliver value to end-users, customers, and stakeholders — while using the corporate resources wisely. right instances of their product More specifically, in a digital context, effective product leadership requires a wide range of skills and competencies, spanning across technology and commercial domains. Regardless of the actual title — Product Manager, Architect, Chief Product Officer, etc. — true Product Leaders tend to have the following nine abilities and talents. 1. Product & User Sense Thinking in terms of products is not as easy as it may sound. After 20 years in the technology innovation and product development space, I’ve witnessed many promising ideas and concepts failing due to insufficient framing, definition, or work prioritization. I’ve met teams struggling to understand the notion of a — and how it is different from a . In other cases, great concepts were abandoned simply due to the limited ability of the team to see the 'big picture' and 'how to get there': the product development process — and especially the agile, iterative approach — is often misunderstood, and thus, not applied in the right way. product project Having the ability to concrete, coherent products with high business potential is a rare skill. Good Product Leaders have the talent to 'easily' obtain a holistic, strategic view of what the right product be for a given problem space; they can identify the assumptions and they know to test them, learn and adapt; they define what success looks like with clarity, and set the measurement framework that helps the team to execute fast — in a truly agile fashion. define might riskier how Having good also requires the ability to : Succesful Product Leaders demonstrate a ‘user mentality’ and use empathy to relate to user problems, frequent customer needs, and stakeholder expectations; they are able to feel the pain points and deeply understand the problem and the ecosystem in which it exists. product sense think 'as a user' The define a product with clarity implies an increased ability to capture and process the wider ‘landscape’ — that is, the key players in the market, their differentiators, the current monetization models, the involved technology, and the partner ecosystem. 2. Innovation Mindset Successful Product Leaders realize the value of innovation for product development and establish the right processes to empower a special culture — fostering , , , and . Mastering innovation practices and tools (such as design thinking, design sprints, idea assessment, prioritization rapid prototyping and validation methods) is essential for this role. ideas sharing collaboration creativeness experimentation Additionally, Product Leaders have the mechanisms to deal with 'failure' - they accept it as part of the innovation process and as a source of learning - in the context of continuous improvement. They encourage their team to take risks and 'fail-safe, fail-fast' as part of a fast-paced, ongoing product innovation process. 3. Entrepreneurial Thinking Product Leaders demonstrate a special mindset that sets the basis for success: they are enthusiastic about designing and building products people love; they are always looking for opportunities to create value and solve big problems in novel and effective ways; they are driven by impact and innovation, but at the same time they stay connected to reality via clear objectives and systematic measurement of performance and success -- after all, they have to also deliver value for the business. To have a successful career in product management, you need but also awareness of the , and — along with the priorities imposed by user demand and needs. As the ‘CEO of the product,’ you will have to balance what users need, what creates a significant business potential for the company, and how to best build it with the available corporate resources (e.g. technology, talent, financial resources). big thinking business commercial, technological constraints 4. Cross-domain Knowledge Modern digital products are complex: their success depends on a mix of technology, usability, business models, marketing, and operational excellence — they must all work together in harmony to drive user engagement, value creation, and sufficient monetization. Product Leaders ‘make things happen’ by applying the vision, defining the roadmap, and wisely steering the efforts of talented that deal with all the above. multidisciplinary teams In a typical scenario, 'product people' interact with designers, software engineers, data scientists, marketing, and commercialization experts and hence it is crucial to be able to understand the language, the perspective, and the capabilities of each domain in the context of the product. When developing digital products, a strong technical background is a must for a great Product Leader: Defining exceptional products depends on the level of understanding of both the capabilities and the constraints of technology. While the Product Leader can delegate certain technical workstreams and decisions to technology experts in the team, it is always a major plus to have a solid technological background and deep understanding of the ‘state of the art’ and the global tech trends. For example, in the context of a consumer-facing digital service, the Product Leader must be able to understand the principles behind a good user experience, the user engagement measurement frameworks, and the underlying enabling technologies -- product people must be able to spot the opportunities arising from the latest technologies but also be aware of the limitations and the associated risks. In the same example, the Product Leader is expected to have a sufficient understanding of Machine Learning models and Data Science technologies— and how these can power smart, differentiated end-user experiences. 5. Commercial Acumen The commercial element is key. Good Product Leaders are able to identify the right business models, monetization strategies, pricing structures, and growth mechanisms to ensure the viability of the product; they continually explore and spot opportunities to introduce - novel features and solutions - along with . They scan the ecosystem not only for competitors but also for great partners and potential synergies. product innovation business model innovation 6. Data-Driven, but with Sound Judgement During the product development journey, it is critical to measure all the key aspects of the engineering/ product development process along with the performance of the actual product instances -- through customer feedback. Product Leaders recognize the importance of effective feedback loops - streams of data that are converted to insights that may or certain important decisions in the product development life cycle. Consequently, a very important quality of a Product Leader is the ability to , and the types of feedback loops that can be used to improve the decision-making process (faster and better decisions). For example, is findings from user-research strong enough to kill a feature? When to use A/B testing to decide on feature variants? How to model, analyze, and interpret user feedback? How to weight qualitative vs quantitative feedback? How to leverage telemetry insights to improve existing features and user experiences? trigger support identify the right sources and classes of data The ability of the Product Leader to is equally critical - how to synthesize insights, qualitative and quantitative data coming from experiments, prototyping, market research, and other sources, in order to make the right product decisions. Successful Product Leaders use these data streams to steer the efforts, adapt the scope and the strategy -- they make those tough decisions that separate success from failure. interpret the data in the right context However, there are cases where the data may be incomplete, unreliable, or even misleading - which emphasizes the importance of another leadership quality: the ability to - or even deciding against the 'data story'. make decisions with limited or no data In ambiguous situations, Product Leaders are expected to demonstrate sound judgment and even question the signals and the patterns derived from data - there may be various reasons to do, such as alternate interpretations of the situation or even a radical, strategic view. Successful leaders know when to be purely ‘data-driven’ and when to lean on their business judgment, critical thinking, and strategic insights. 7. Ability to Execute, Readiness to Pivot Defining great products is only one part of the story. As a Product Leader, you need to set the roadmap, make the team believe the and the of the product, drive execution, and ‘make it happen’; and this requires a number of special skills and qualities, including effective work decomposition, prioritization, measurement and steering of the overall product development process. purpose potential When building digital products, great execution also means , , , and also . Leaders should be ready to accept failure and recognize early signals indicating a different approach or a new path. By setting well-thought goals and success criteria, good Product Leaders continuously measure the performance of experiments and newly released features and they are ready to reconsider even core aspects of the initial product strategy. pace effectiveness agility readiness to pivot This agility also requires effective communication with the product team -- great Product Leaders are transparent, they share insights about failures and justify with clear articulation any changes in the strategy or the focus of the product. They bring clarity on how the product needs to evolve and why. 8. Ability to Streamline Product Innovation In a modern product development setup, there are plenty of methods, techniques. systems and tools that could be used to accelerate and streamline the process. Product Leaders must be able to establish the right process and activate those methods that allow fast execution, continuous discovery of high-potential ideas, and smooth adaptation of the product based on insights and just-in-time information and knowledge. For example, Product Leaders need to tell when a prototyping effort or a proof of concept is required and how the outcomes could impact those critical pending decisions. They must introduce fast, effective processes to incorporate new ideas into existing product backlogs along with a transparent assessment and prioritization framework. 9. A 'Product' Leadership Style There’s a lot written about different leadership styles and the ‘effective business leader’. But in my opinion, is very different from . Yes, as a Product Leader you need to inspire and energize your team and all the key stakeholders — but this should ideally happen ‘naturally,’ as the outcome of the extraordinary efforts to shape and build a great product. leading people leading a product Leading a product is also about communicating the vision, the purpose, and the strategy — as part of an open culture of innovation, information sharing, and collaboration. It is this combined with the and that inspires people to do their best and get obsessed with product success. special culture bold product vision passion for impact When people believe in the purpose of the product and the commitment of its leadership, they engage, they become part of it, and ultimately part of its success. In this ‘open’ mode, people realize that they can leave their mark on the product, get recognized, and grow as leaders; leading great products to success also leads to great product engineering teams. As you develop your leadership style, you should focus inspiring influencing managing people. on and over Great products are built by talented teams that are inspired by passionate, skillful product visionaries. To become one, you need a natural propensity to build epic stuff, passion for impact, and a wide combination of skills — from software engineering, and product development to monetization and growth. Also published in The Innovation Mode Blog cover image: Bonneval Sebastien on Unsplash

The 9 Character Traits of Great Product Leaders

This paper is available on under CC 4.0 license. arxiv Authors: (1) Jakub DRÁPAL, Institute of State and Law of the Czech Academy of Sciences, Czechia, Institute of Criminal Law and Criminology, Leiden University, the Netherlands; (2) Hannes WESTERMANN, Cyberjustice Laboratory, Université de Montréal, Canada; (3) Jaromir SAVELKA, School of Computer Science, Carnegie Mellon University, USA. Table of Links Abstract & Introduction Related Work Dataset Proposed Framework Experimental Design Results and Discussion Conclusions, Future Work and References Abstract. Thematic analysis and other variants of inductive coding are widely used qualitative analytic methods within empirical legal studies (ELS). We propose a novel framework facilitating effective collaboration of a legal expert with a large language model (LLM) for generating initial codes (phase 2 of thematic analysis), searching for themes (phase 3), and classifying the data in terms of the themes (to kick-start phase 4). We employed the framework for an analysis of a dataset (n = 785) of facts descriptions from criminal court opinions regarding thefts. The goal of the analysis was to discover classes of typical thefts. Our results show that the LLM, namely OpenAI’s GPT-4, generated reasonable initial codes, and it was capable of improving the quality of the codes based on expert feedback. They also suggest that the model performed well in zero-shot classification of facts descriptions in terms of the themes. Finally, the themes autonomously discovered by the LLM appear to map fairly well to the themes arrived at by legal experts. These findings can be leveraged by legal researchers to guide their decisions in integrating LLMs into their thematic analyses, as well as other inductive coding projects. Thematic analysis, empirical legal studies, criminal law, large language models, generative pre-trained transformers, GPT-4 Keywords. 1. Introduction Empirical legal studies (ELS) is an approach to the study of law through empirical methods typical of economics, psychology, and sociology. Since law is a heavily text-based discipline ELS frequently focuses on text analytic methods, including deductive and inductive coding. Deductive coding focuses on applying a fixed set of codes to a dataset, whereas inductive coding leads to a simultaneous discovery of the codes from the data and their application. While investigations into various methods to support deductive coding have attracted much recent attention in AI & Law [1,2,3] very few studies focused on inductive coding [4]. One popular inductive coding method is “thematic analysis” [5]. J. Drápal, H. Westermann, and J. Savelka / Using LLMs to Support Thematic Analysis in ELS While rarely explicitly acknowledged thematic analysis is widely used in ELS. Figure 1 shows the six phases of the analysis, starting from the raw data and finishing with the scholarly report on the studied phenomena. We propose a novel LLM-powered framework to support a subject matter expert in performing phases 2 and 3 of the analysis. We employed the proposed framework in a thematic analysis of criminal courts’ opinions, focused on the criminal offense of theft in Czechia. Criminal offense categories (e.g., theft, murder) are usually defined in statutory law while the individual criminal acts are described by courts when they apply the law to factual circumstances of the cases. An important question in criminal law and criminology is what behaviors are actually criminalized and whether it is done appropriately. Neither the statutory definitions of the offenses (they are too general and not “sociologically relevant” [6]) nor the descriptions of the factual circumstances from cases (too specific) can answer the question. To get insight into what behavior is criminalized and how effectively, it is necessary to identify shared features of criminal acts, generalize them into “typical crimes” [7,8] and arriving at behavioral-based categories [9,10]. This is akin to performing thematic analysis. While important, such analysis is an expensive and time-consuming endeavour. Hence, a (semi-)automated approach would be useful. To assess the capability of a state-of-the-art LLM (GPT-4) to support selected stages of the thematic analysis, we investigated the following research questions: (RQ1) How successfully can the LLM perform initial coding of the data? (RQ2) To what degree can a subject matter expert improve the quality of the initial codes via natural language feedback? (RQ3) How successfully can the LLM predict themes for the analyzed data points? (RQ4) How successfully can the LLM autonomously discover themes and associate them with the analyzed data? This paper is available on under CC 4.0 license. arxiv

How to Use Large Language Models to Support Thematic Analysis in Empirical Legal Studies

PwC predicts that healthcare costs will . This increase is primarily attributed to healthcare workers experiencing burnout, the subsequent workforce shortage, disputes between payers and providers, and inflation. To ensure efficient patient care without incurring excessive operational costs, the industry is exploring innovative technologies, such as generative AI in healthcare. go up by 7% in 2024 Accenture reports that can be enhanced with AI, while a recent Forbes article suggests that this technology can spare the US medical sector . 40% of healthcare providers’ working hours at least $200 billion in annual expenses in healthcare uses machine learning algorithms to analyze unstructured data, such as patient health records, medical images, audio recordings of consultations, etc., and produce new content similar to what it has been trained on. Generative AI In this article, our generative AI development company will explain how the technology can support healthcare organizations. Generative AI use cases in healthcare Facilitating medical training and simulation Assisting in clinical diagnosis Contributing to drug development Automating administrative tasks Generating synthetic medical data Facilitating medical training and simulations can come up with realistic simulations replicating a large variety of health conditions, allowing medical students and professionals to practice in a risk-free, controlled environment. AI can generate patient models with different diseases or help simulate a surgery or another medical procedure. Generative AI in healthcare Traditional training involves pre-programmed scenarios, which are restrictive. AI, on the other hand, can quickly generate patient cases and adapt in real-time responding to the decisions the trainees make. This creates a more challenging and authentic learning experience. Real-life example The University of Michigan built a generative AI in healthcare model that can produce various scenarios for . simulating sepsis treatment The University of Pennsylvania to simulate the spread of COVID-19 and test different interventions. This helped the researchers evaluate the potential impact of social distancing and vaccination on the virus. deployed a generative AI model Assisting in clinical diagnosis Here is how generative AI for healthcare can contribute to diagnostics: . Hospitals can employ generative AI tools to enhance the traditional AI’s diagnostic abilities. This technology can convert poor-quality scans into high-resolution medical images with great details, apply , and present the results to radiologists. Generating high-quality medical images anomaly detection AI algorithms . Researchers can train generative AI models on medical images, lab tests, and other patient data to detect and diagnose early onsets of different health conditions. These algorithms can spot , , , , , and more. Additionally, AI models can unveil biomarkers that can cause particular disorders and predict disease progression. Diagnosing diseases skin cancer lung cancer hidden fractures early signs of Alzheimer’s diabetic retinopathy . Diagnosticians can turn to generative AI in healthcare if they have questions instead of looking for an answer in medical books. AI algorithms can process large amounts of data and generate answers fast, saving doctors precious time. Answering medical questions Real-life examples A team of researchers to extract and enhance features in low-quality medical scans, transforming them into high-resolution images. This approach was tested on brain MRI scans, dermoscopy, retinal fundoscopy, and cardiac ultrasound images, displaying a superior accuracy rate in anomaly detection after image enhancement. experimented with Generative Adversarial Network (GAN) models In another example, was trained on the MedQA dataset and achieved an 85% accuracy rate while answering relevant medical questions. Google admits that the algorithm still needs improvement, but it’s a strong start for generative AI as a diagnostics assistant. Google’s AI-powered Med-Palm 2 Contributing to drug development According to the Congressional Budget Office, the process of new drug development , which also includes failed drugs. Fortunately, there is evidence that AI has the potential to cut the time needed to design and screen new drugs almost by half, saving the pharma industry around in the process. Additionally, this technology can reduce costs associated with clinical trials by $28 billion per year. costs on average $1 billion to $2 billion $26 billion in annual expenses Pharmaceutical companies can deploy generative AI in healthcare to speed up drug discovery by: Designing and generating new molecules with desired properties that researchers can later evaluate in lab settings Predicting properties of novel drug candidates and proteins Generating virtual compounds with high binding affinity to the target that can be tested in computer simulations to reduce costs Forecasting side effects of novel drugs by analyzing their molecular structure You can find more information on the role of AI in drug discovery and how on our blog. it facilitates clinical trials Real-life examples The rise of strategic partnerships between biotech companies and AI startups is an early sign of generative AI taking over the pharmaceutical industry. Just recently, Recursion Pharmaceuticals for $88 million. One of them, Valence, is known for its generative AI capabilities and will work on designing drug candidates based on small and noisy datasets that are not sufficient for traditional drug discovery methods. acquired two Canadian AI startups Another interesting example comes from the University of Toronto. A research team built a generative AI system, ProteinSGM, that after studying imagery representations of existing protein structures. This tool can produce proteins at a high rate, and then another AI model, OmegaFold, is deployed to evaluate the resulting proteins’ potential. Researchers reported that most of the novel generated sequences fold into real protein structures. can generate novel realistic proteins Automating administrative tasks This is one of the most prominent generative AI use cases in healthcare. Studies show that burnout rate among physicians in the US has . Doctors suffering from this condition are more likely to be involved in incidents endangering their patients and are more inclined to alcohol abuse and having suicidal thoughts. reached a whopping 62% Fortunately, generative AI in healthcare can partially alleviate the burden off the doctors’ shoulders by streamlining administrative tasks. It can simultaneously reduce costs associated with administration, which, according to HealthAffairs, of overall healthcare spending. Here is what generative AI can do: accounts for 15%-30% Extract data from patients’ medical records and populate the corresponding health registries. Microsoft is planning to . This tool will perform various administrative tasks, such as replying to patient messages. integrate generative AI into Epic’s EHR Transcribe and summarize patient consultations, fill this information into the corresponding EHR fields, and produce clinical documentation. Microsoft’s into its clinical transcription software. Doctors can already test the beta version. Nuance integrated generative AI tech GPT-4 Generate structured health reports by analyzing patient information, such as medical history, lab results, scans, etc. Produce treatment recommendations Answer doctors’ queries Find optimal time slots for appointment scheduling based on patients’ needs and doctors’ availability Generate personalized appointment reminders and follow-up emails Review medical insurance claims and predict which ones are likely to be rejected Compose surveys to gather patient feedback on different procedures and visits, analyze it, and produce actionable insights to improve care delivery Real-life example , a medical AI startup, built a generative AI assistant that helps doctors tackle administrative duties more efficiently. This tool can access patient data, including EHRs, insurance claims, and scanned documents, give status updates, recommend care options, and answer doctors’ questions. It can even generate structured documents, such as referral letters and progress notes. Navina Navina has already scored $44 million in funding, which indicates a strong interest from the medical community. Generating synthetic medical data Medical research relies on accessing vast amounts of data on different health conditions. This data is painfully lacking, especially when it comes to rare diseases. Also, such data is expensive to gather, and its usage and sharing are governed by privacy laws. Generative AI in medicine can produce synthetic data samples that can augment real-life health datasets and are not subject to privacy regulations, as the healthcare data doesn’t belong to particular individuals. Artificial intelligence can generate EHR data, scans, etc. Real-life examples A team of German researchers built an AI-powered model, GANerAid, to . This model is based on the GAN approach and can produce medical data with the desired properties even if the original training dataset was limited in size. generate synthetic patient data for clinical trials Another team of scientists experimented with generative AI to . The researchers were motivated by restrictive data privacy regulations and the inability to effectively share patient data between hospitals. They built the EHR-M-GAN model that could derive heterogeneous, mixed-type EHR data (meaning it contains both continuous and discrete values) that realistically represents patient trajectories. synthesize electronic health records Ethical considerations and challenges of generative AI in healthcare Even though tech and consulting giants , we can also see how prominent AI experts, including Tesla CEO Elon Musk and OpenAI CEO Sam Altman, warn of the risks associated with the technology. So, which challenges does generative AI bring to healthcare? continue to invest in AI . AI models’ performance is as good as the dataset they were trained on. If the data does not fairly represent the target population, this will leave room for bias against less represented groups. As generative AI tools train on vast amounts of patient records data, they will inherit any bias present there, and it will be a challenge to detect, let alone eradicate it. Bias . Even though AI presents considerable ethical concerns, there are no official regulations yet to govern the use of this technology. The towards formalizing relevant policies, but this won’t take place in the near future. Lack of regulations US and the EU are working . AI does make mistakes, and in healthcare, the price of such mistakes is rather high. For instance, large language models (LLMs) can . Meaning they can produce syntactically probable outcomes that are factually incorrect. Healthcare organizations will need to decide when to tolerate errors and when to require the AI model to explain its conclusions. For instance, if generative AI is used to assist in cancer diagnosis, doctors are unlikely to adopt such a tool if it can’t justify its recommendations. Accuracy concerns hallucinate . Who is responsible for the final health outcome? Is it the doctor, the AI vendor, the AI developers, or yet another party? Lack of accountability can have a negative impact on motivation and performance. Accountability Ready to enhance your healthcare practice with generative AI? Generative AI algorithms are becoming increasingly powerful. Robert Pearl, a clinical professor at Stanford University School of Medicine, : said “ChatGPT is doubling in power every six months to a year. In five years, it’s going to be 30 times more powerful than it is today. In 10 years, it will be 1,000 times more powerful. What exists today is like a toy. In next-generation tools, it’s estimated there will be a trillion parameters, which is interestingly the approximate number of connections in the human brain.” AI can be a powerful ally, but if misused, it can cause significant damage. Healthcare organizations need to approach this technology with caution. If you are considering deploying AI-based solutions for healthcare, here are three tips to get you started: . Even if you decide to opt for a pre-trained, ready-made AI model, you might still want to retrain it on your proprietary dataset, which needs to be of high quality and representative of the target population. Keep medical data secure at all times and safeguard patient privacy. It would be useful to disclose which dataset an algorithm was trained on as it helps to understand where it will perform well and where it might fail. Prepare your data . Cultivate the concept of responsible AI in your organization. Make sure people know when and how to use the tools and who assumes responsibility for the final outcome. Test the generative AI models on use cases with limited impact before scaling to more sensitive applications. As mentioned earlier, generative AI can make mistakes. Decide where a small failure rate is acceptable and where you can’t afford it. For instance, 98% accuracy can suffice in administrative applications, but it’s unacceptable in diagnostics and patient-facing practices. Devise a framework that will govern the use of generative AI in healthcare at your hospital. Take control of your AI models . AI still needs human guidance, especially in the heavily regulated healthcare sector. remains an essential ingredient for the technology to succeed. The medical and administrative staff will be expected to supervise AI models, so hospitals need to focus on training people for this task. Employees, on the other hand, should be able to reinvent their daily routine, now that AI is a part of it, to use the freed-up time to produce value. Help your employees accept the technology and use it Human-in-the-loop Want to benefit from generative AI but not sure how to proceed? ! We will help you prepare your data, implement the tool, and integrate it into your operations. Drop ITRex a line Also published here.

Generative AI in Healthcare: Transforming Diagnosis, Drug Development, and More

Experts at the Institute for the Future say that about 85% of the jobs that today's college students will have in 2030 haven't actually been invented yet. We’ll do our work differently, too, according to Dell. In its , the company found that by 2030, 67% of business leaders expect to use new technologies to create equal opportunities by removing human bias in decision-making, 86% plan to use emerging technologies to improve workforce productivity, and 70% would welcome workers partnering with machines/robots to surpass their human limitations. future of work research The scale of technological advancements we are currently undergoing, thanks to advances in AI, machine learning, robotics, and other technologies, has increased this pace of change tenfold, , adding that “we can expect to experience more progress in the next decade than in the past 100 years combined.” says McKinsey McKinsey estimates that by 2025, 50 billion devices will be connected to the Industrial Internet of Things (IIoT), low-code/no-code (LC/NC) technology will also be used by 70% of new applications by 2025, and the global metaverse revenue opportunity could approach $800 billion next year. New jobs are emerging New jobs emerge all the time thanks to the advances in technology, and most recently, generative AI technologies have come to the fore. But AI is just one area where new job titles are rapidly emerging. We can expect to see jobs for sentiment analysis, AI trainers, AI compliance managers and prediction analysers (among many others) emerge over the next few years. There are other sectors in which new jobs will emerge, too. As IoT, augmented or extended reality, the metaverse, and blockchain technologies intertwine and embed themselves increasingly into our everyday lives; recent research has identified 10 potential key job titles of the future. They are: Chief automation officer (CAO) Gamification marketing specialist Metaverse research scientist Metaverse storyteller Cybersecurity threat attribution analyst VR programmer Human-machine matchmaker AR journey builder Data detective Digital reputation defender What about getting hired now? While some of the jobs above are just emerging or are still a way off, there is plenty of scope for those in the tech sector right now. According to the Bureau of Labor Statistics' , there are 20 occupations with the highest projected percent change of employment between up to 2031. These include data scientists, information security analysts, and web developers. fastest growing occupations report So, if you are in the market for a new tech job, then the should be the place you begin your search. Hackernoon Job Board It contains thousands of roles to apply for, and you can discover three exciting opportunities currently on offer below. Data Scientist, SAIC, Chantilly SAIC is seeking a to fill a critical position. You’ll perform data processing and analysis tasks, working with test designers, statisticians, and other data scientists to build datasets and create analytic products as well as work with senior team members to perform statistical analyses to identify significant effects in novel experimental and operational datasets, including exploratory analyses. A Bachelor's in computer science, data science, or a quantitative science, or similar degree is required, along with two years’ or more experience. You’ll be required to have Active Top Secret/SCI with the ability to get a Poly with the NRO, as well as have experience using Python or R. . Jr Data Scientist Get more details now Senior Frontend Engineer, Remote, Givebutter, Wilmington Fundraising platform Givebutter allows nonprofits to replace multiple tools so they can launch fundraisers and events, use donation forms and donor management (CRM), send emails and text blasts. It is hiring a to contribute to building Givebutter’s products, primarily the CRM platform, perform peer code reviews, ensuring that code meets acceptance criteria, adheres to company code guidelines and follows engineering best practices and work closely with the design team using Figma to build out components and features. . Senior Frontend Engineer View additional details here Information Security Engineer, PayPal, Austin PayPal is seeking a for the Threat Management Engineering team in the Enterprise Cyber Security Org. In this role, you will develop, support, tune and deploy web application firewall security solutions across PayPal. Day-to-day you’ll perform hands-on web application firewall deployment, configuration, policy fine-tuning and maintenance, and investigate and analyze data using a SIEM tool such as Splunk. Among the requirements you’ll need for this role are five years’ of experience in cybersecurity engineering, with experience that includes configuring and managing web application firewalls. . Security Engineer See all the requirements here For thousands more jobs across tech, visit the Hackernoon Job Board today

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