Behavioral Signals (Behavioral Signal Technologies, Inc.) develops technology to analyze human behavior from voice-data. Transform your customers’ experience with AI-Mediated Conversations technology. Automatically match each customer to the best-suited agent using voice data and emotion AI, and see the overall performance and outcomes of your conversations reach higher levels

Behavioral Signals

behavioralsignals

HackerNoon Reporter: Please tell us briefly about your  background. I’m the CEO at Behavioral Signals, an enterprise software company that develops AI technology to analyze human behavior from voice data. In 2012, I led private equity-backed Cricut's turnaround from bankruptcy to profitability. Cricut held its $4.4 billion IPO in April 2021 and is worth approximately $8B today. In 2014, I founded TiZE, ML-based cloud software for specialty chemicals which was acquired in 2016. Among other companies, I’ve worked in leadership positions for Logitech and Kronos Inc. What's your startup called? And in a sentence or two, what does it do? Behavioral Signals is an enterprise artificial intelligence software company that excels at distinguishing signals in speech data with its proprietary deep learning technology. Human communication is a complex process that depends on not just the words being spoken but also the way they are expressed. Our flagship product AI-MC uses the power of our patented tonality engine to build rich behavioral profiles (conversational bioprints) for parties involved in the conversation. AI-MC automatically matches each customer to the best-suited agent using voice data and emotion AI and affects the overall performance and outcomes of call center conversations. What is the origin story? The company founders, Alex Potamianos and Shri Narayanan, are accomplished scientists and academicians. Shri, who is a founder and executive director of the coveted SAIL institute at USC, built early models that analyze human emotions and behaviors from the tone of voice and used these models to increase engagement in emotionally challenged situations such as interactions with autistic kids. Behavioral Signals was spun out of USC in 2016 and has since focused on more CX-centered use cases. What do you love about your team, and why are you the ones to solve this problem? Our team includes accomplished researchers and senior engineers that deeply understand natural language and behavioral signal processing. The goal of pushing the boundaries of current technology is at the core of what we do. Our algorithms analyze human emotions and behaviors, transform data into usable information, and lead to making better business decisions. Until now, human emotion has been considered impossible to quantify and impossible to measure. With our patented analytics engine, we have brought to market a science to measure and interpret the “how” part of human interactions. We are bridging the communication gap between humans and machines by introducing emotional intelligence, from speech, into conversations with AI. Based on extensive research spanning 2 decades, we have created an AI technology that enables matching a customer to the best-suited agent to handle a specific conversation. Humans have specific behaviors and traits that help us get along with some people better than with others. Our technology taps into this knowledge and equips our clients to improve call-related outcomes and customer satisfaction. If you weren’t building your startup, what would you be doing? I am an early-stage investor and sit on boards of several companies. I love leveraging my experience to help young startup founders grow both personally and professionally. I’m also an avid cyclist and tend to spend a fair share of my free time on the trails. I coach the NICA mountain biking team at Monte Vista High School. At the moment, how do you measure success? What are your core metrics? Up till now, communication with customers usually involved random pairing between employee and customer, regardless of a customer profile or employee skillset. Often that did not work well, causing friction, diminished customer satisfaction, and lost revenues. Regardless of the type of business communication, a sales call, a support call, or a revenue collection call, the conversation dynamics between the two participants are rarely identical. We have specific behaviors and traits that help us get along with some people better than with others. This is where AI-MC comes in. It automatically matches each customer to the best-suited employee, using Emotion AI and Behavioral Signal Processing, and empowers employees to perform better in each call. Our AI-powered match-making engine impacts several core metrics such as CX, FCR, Call-Time, Revenue Recovery, etc. Our deployments in the industry have consistently delivered a 12-17% increase in revenue recovery, +8% increase in call success ratios, and over 10% increase in customer satisfaction. What’s most exciting about your traction to date? The fact that we’re getting interest from companies from all over the globe, regardless of language. We derive signals from the tone of voice, basically ‘how’ something is being said and not the ‘what’ or words being spoken. This ‘special sauce’ allows us to be completely language agnostic. We have clients that speak English but also Spanish, Russian, Greek, and other languages. An added benefit is that an absence of using a transcription engine frees us from aspects such as ‘redaction.’ There is simply no text to redact! This enables us to be GDPR and SOC2 compliant, which is a key ask from our clients in the financial sector. What technologies are you currently most excited about, and most worried about? And why? Ethics, bias, privacy, and security are important considerations in all technologies. This is especially true for AI. Many people think of AI as a black box, often as a tech that is bound to get out of control. This is not true, but AI may involve complex neural engines that we may not be able to effectively influence at some point. This is the concern with Singularity. AI has fantastic possibilities to accomplish things beyond a human’s reach. AI has the potential to improve our lives in health, education, transport, communications, and within our homes. Having said that, I believe it’s imperative that the decision-makers have a clear understanding of the technologies involved and make informed choices that take into consideration aspects beyond just profit-taking. Privacy, ethical use, security, unbiased outcomes should always be of primary importance. What drew you to get published on HackerNoon? What do you like most about our platform? Hackernoon is my go-to place for great tech content, and I love the cultural platform you have created. I’m a fan of your work and grateful to be a part of this community. What advice would you give to the 21-year-old version of yourself? “Don’t play too safe. It’s not worth the risk!”. Our entire mindset around risk-taking is entirely skewed by society. What we hear about taking risks, and about what risk actually is, and what it means not to take a risk is entirely out of date. The other thing of importance is to cultivate an early ability to see the forest through the trees. One always needs to look beyond what you’re currently working on. What often holds smart people back is their tendency to become so focused on what’s currently surrounding them that they lose their grasp of what’s ahead. What is something surprising you've learned this year that your contemporaries would benefit from knowing? I’ve learned that we are a lot more resilient than we think we are. Resilience is hard to assess as it differs from other concepts such as mindset or adaptability because it happens in the face of unexpected or difficult circumstances. To build your resilience skills in the new year, try some of these strategies: View your decisions as active choices - not sacrifices. Believe in yourself more View stress and setbacks as opportunities to push yourself. Vote for us as LA Startup of the Year: https://startups.hackernoon.com/california/los-angeles

Behavioral Signals Analyzes Human Behavior from Voice Data

is an entrepreneur, speaker, investor, and CEO at , an emotion AI and behavioral recognition software company. Rana Gujral Behavioral Signals In 2014, founded TiZE, a cloud software for specialty chemicals, and held the role of CEO until his exit when TiZE was acquired by Alchemy. He has been awarded the ‘Entrepreneur of the Month’ by CIO Magazine, the ‘US-China Pioneer’ Award by IEIE, and listed in "Top 10 Entrepreneurs to follow in 2017" by Huffington Post. Rana Due to his notable thought-leadership in the voice-first community, spearheading digital transformation in the behavioral analysis space, he is recognized in Inc. Magazine as an “AI Entrepreneur to Watch”. His writing can be found in publications such as Inc., TechCrunch, Forbes and Hacker Noon. now and for discussion on Ask Rana anything tune in September 30th, 9 am PST. Vote for Rana in , . Hacker Noon Contibutor of the Year: CHATBOTS , Hacker Noon Contibutor of the Year: ARTIFICIAL INTELLIGENCE ML Writer of the Year Join the AMA. Ask a question.

AMA with Rana Gujral, CEO of Behavioral Signals and Noonies Nominee, on September 30th, 9 am PST

The year 2021 was a banner year for digital health with around invested into the sector. Telemedicine and mental health were among the verticals that received the most investment dollars. It was also the year when value-based healthcare became evidently complemented by technology, a tendency being accelerated by the pandemic. $57 billion The growing momentum of health tech, in turn, makes VCs bullish on the long-term potential of health tech, especially in telemedicine, biopharma, and medical devices. But will the momentum stay the same for health tech in 2022? Let’s find out. The state of digital health in 2022 According to , the global digital health funding has dropped in Q2’2022 compared to 2021. Thus, it now stands at $7.1 billion, falling 32% from Q1'22 and 58% from the highest watermark in Q2' 21. The decrease has influenced almost all digital health areas, yet leaving health IT at the same level. CBInsights Digital health - a miscellany of software, technology, wearables, and applications that cater to the consumer, i.e. patient. Health Informatics or IT - software designed specifically for healthcare providers that aim to make the process of tending to a patient more accurate. The global digital health market has also experienced a funding decrease of 57% as a result of supply chain woes, a volatile stock market, and turbulent geopolitics. But there’s still a bright side for digital health in 2022. Europe, for example, has seen an increase of 6% in funding growth, while health IT funding grew by 27%. And although the unicorn landscape is not as rich as it was in 2021, this year has still welcomed some disruptors, including Viz.ai, BostonGene, and others. Now let’s see what areas have reaped the most buzz or are poised to do so beyond 2022. What Impacts The Industry in 2022? The healthcare industry has seen the advent of a wide range of innovations over the last few years. Yet, there’s a common denominator that links all those applications and that’s technology. In particular, the market for artificial intelligence in healthcare is estimated to reach over by 2030. So let’s see how machine intelligence has toppled ours. $194 billion Digital therapeutics funding reaches over $1 billion in 2022 YTD DTx or digital therapeutics is an emerging field of healthcare that uses digital technologies to prevent, manage, and treat medical conditions. Unlike traditional treatment and medical devices, digital therapeutics leverage technology to target disease at the level of behavior or physiology. This allows it to be used to treat a wide range of conditions, from chronic diseases like diabetes to mental health conditions like anxiety and depression. DTx’s can be prescribed by physicians as stand-alone treatments or complement drug-based therapies. In simple words, DTx are complex applications that rely on real health outcomes and clinical evidence to treat a wide range of diseases and disorders. Typically, they are approved by the FDA or undergo any other form of regulatory scrutiny. As you may have guessed, (ML and NLP, to be exact) that gave birth to digital therapeutics. And with smartphones in everybody’s pocket, delivering DTx has become easier than ever before. it’s data processing and artificial intelligence for pain relief to be granted Food and Drug Administration approval. EaseVRx is a prescription-use virtual reality headset that helps with pain reduction. Device users reported, on average, a 42 percent reduction in pain intensity, . AppliedVR's EaseVRx became the first virtual reality-based digital therapeutic according to one study According to a , people who used a device reported a 42% pain reduction. study Mental health tech raises $1.5 billion in 2022 YTD Sadly, mental illnesses aren’t rare in the United States. Thus, almost one in five U.S. adults suffers from a mental illness. That’s almost people. 53 million Mental health tech is a growing field of technology that is designed to help people with mental illness. This includes things like mood trackers, apps that help you manage your medications, and even virtual reality therapies. Mental health tech is a growing field because mental illness is a major problem in the world, and we need new ways to treat it. Thanks to machine intelligence, therapists can now comb through large amounts of historical data to track family histories, past behaviors, and responses to prior treatments. Combined, this information empowers therapists to better diagnose a patient and choose the most effective treatment. in the future includes: The big promise AI holds for mental health Affordability - mobile applications are cheaper compared to common expenses associated with in-person appointments (therapists’ rates, commute, etc.) Anonymity - over choose robots over humans to discuss their mental health issues. 80% of people Efficiency - smart algorithms have already in interpreting behavioral signals. proven to be efficient The application matrix of mental health tech has significantly grown over the last few years and is slated to gain traction in the future. Remote patient monitoring, mental health apps, virtual reality, and CBT apps are just a sliver of everything that mental health tech has to offer , an online mental health platform, developed by The App Solutions. Calmerry Clinical trials tech raises over $1 billion in 2022 YTD Clinical trials are a critical step in the development of new treatments and therapies. They provide valuable data that can help us understand how a new treatment works, what side effects it may cause, and whether it is safe and effective. And you know what’s shocking? to hit the market. In the United States, it takes an average of (!)  for an experimental drug to make it to the medicine cabinet. Moreover, only 5 in 5,000 drugs go from preclinical testing progress to human testing. The number of years it usually takes for a new drug 12 years In recent years, AI has been used increasingly to reduce the time and costs it takes to develop a new drug. In clinical trials, algorithms can help to identify which patients are most likely to respond to a new treatment. Machine learning can also help to monitor patients during the trial to ensure that they are adhering to the treatment plan. According to , AI-powered applications play a crucial role at all stages of clinical trials - from trial design to sign-off. Advanced data analytics and automation are the main unique selling propositions of AI for drug development. Deloitte In the future, computer intelligence is projected to bolster the design and execution of faster, and more patient-focused clinical trials by processing structured and unstructured patient data in real time. This analysis will be complemented with data from patient populations to generate 360-degree evidence. Telehealth funding hits over $5 billion across 290 deals in 2022 YTD Until the pandemic, only a few tech scions thought of telemedicine as a viable solution with long-term potential. According to a poll, in January 2022, received care via telehealth. In 2021, this figure was around 48%. But despite the drop, it's still higher than before the pandemic 38% of patients is the delivery of healthcare services using telecommunications and digital technologies. It allows patients to consult with healthcare providers from a distance using video conferencing, telephone, or the internet. Telehealth can be used to provide a wide range of services, including primary care, mental health care, and specialty care. Telehealth platforms Telemedicine bring benefits to both types of users: Doctors can securely exchange files with patients via secured live chat, provide quick diagnoses, receive laboratory tests, consult a patient, and others. Patients can receive high-quality medical services without leaving their homes, schedule appointments, pay for medical services online, and others. The pandemic has reinforced the need for virtual health care. Our team, for example, has built a custom for a Berlin-based clinic that needed a remote communication solution to decrease the number of offline visits during the pandemic. telemedicine platform Here's what a telemedicine platform looks like - a conventiently fit into your smartphone. healthcare hub Health IT funding totals $5.1 billion across 254 deals in 2022 YTD Finally, health informatics has got a boost in 2022 as healthcare providers flocked to Unlike traditional health data storing, electronic records allow for rapid information sharing, reduced paperwork, and better follow-up - things much-needed in the modern healthcare system. electronic health records , e-prescribing, and personal health records. Health IT can free you to focus on your health instead of on the everyday hassles of managing your health care. [1] According to , the global healthcare IT market size was valued at over $250,577 million in 2020 and is estimated to garner over $880,688 million by 2030. The full saturation of health IT is only one piece of the puzzle for resolving a system-wide lack of interoperability. statistics Technology Is Making Healthcare More Human The last few years have laid bare the inefficiencies of the traditional healthcare system. , and other problems significantly impact patient outcomes and adversely affect the public perception of traditional healthcare. Lack of visibility, labor shortages, preventable medical errors Paired with technology, the healthcare system can unlock more interoperability, reduce medical errors, and cut down operational costs. So it’s no wonder that we’re moving to a completely new, digital plane of medicine where artificial intelligence, data analysis, and connectivity step in to make our lives better. [1] Health IT: Advancing America’s Health Care. The Office of the National Coordinator for Health Information Technology. https://www.healthit.gov/sites/default/files/pdf/health-information-technology-fact-sheet.pdf

Transformative Healthtech Ideas That Can Solve Healthcare Crisis 

Ideas transformadoras de tecnologías de la salud que pueden resolver la crisis de la atención médica

Idées transformatrices de technologies de la santé qui peuvent résoudre la crise des soins de santé

स्वास्थ्य देखभाल संकट को हल करने वाले परिवर्तनकारी हेल्थटेक विचार

ヘルスケアの危機を解決できる革新的なヘルステックのアイデア

Idéias transformadoras de tecnologia de saúde que podem resolver a crise da saúde

Ý tưởng về Healthtech mang tính chuyển đổi có thể giải quyết cuộc khủng hoảng về chăm sóc sức khỏe

可以解决医疗危机的变革性医疗科技理念

As in all elections of great importance, tallying the official results of Hacker Noon 's annual awards took us somewhat longer than expected. That's because this year’s were bigger, better and greener than anything, ever before: Noonies Awards of you were nominated for one (or more) of the 2,587 Hacker Noon awards up for grabs this year, 221 across award categories: , , , , and . 5 Technology Decentralization Software Development Future Heroes Back to the Internet weeks later: 8 over 200 Noonie Nominee Interviews , AMAs , and Podcasts are live on Hacker Noon , and a whopping votes have been cast over at which means only one thing: 232,444 noonies.tech , The Winners of the Internet for 2020 can now be officially declared! 🥳 Head over to noonies.tech to check out all 221 Noonies Winners (and 442 Runner-Ups!) right now. With very special thanks to who made the 2020 Noonies possible: , , , , , , and ! the Hacker Noon Partners Sustany Capital .TECH Domains Grant for the Web Skillsoft Flipside Crypto Udacity Beyondskills Scroll down to view a selection of top Noonies awards and winners from this year's Hacker Noon awards, or view all on NOONIES.TECH 🤖 🏆 Best Web Monetization Innovator —presented in partnership with GRANT FOR THE WEB A huge thanks to for this award's prize, which includes: Grant for the Web A $1000 award for the eventual winner (!!!) The winner will also receive a 1 year registration for a .tech domain. And the winner is... 1,774 votes) 🥇 Jasmin Virdi ( An open-source contributor who innovated around the Ideas of Web Monetization by submitting 3 project ideas that involve a web component, a Vue plugin, and a starter kit for the Electron App using the Web monetization technology. Runner-ups: 936 votes) 🥈 gFam.live ( gFam is a project that allows content consumers to tip content creators instantly. Creators also receive micropayments when Coil subscribers view their content 🥉 Sharon Wang Co-creator of the Akita web monetization browser extension. The extension makes it easy to learn about and try out Web Monetization! Also has posted some informative articles on dev.to. She's part of the movement making Web Monetization super fun to get involved with, is genuinely friendly and supportive, and cares about the cause. 🏆 Top Programming Guru —presented in partnership with .TECH Domains A huge thanks to for this award's prizes, which include: .TECH Domains $ 3000 Cash Prize + A Free 10-yr domain; 1st Runner-up gets Free 5-yr domain; 2nd Runner-up gets a 3-yr domain And the winner is... ,695 votes) 🥇 Programming with Mosh (30 He trains novice and professional software engineers to increase their skills through his courses, make more money and ultimately change their lives for the better. Runner-ups: ,810 votes) 🥈 Traversy Media (17 Traversy Media features the best online web development and programming tutorials for all of the latest web technologies including Node.js, Angular 2, React.js, PHP, Rails, HTML, CSS and much more 🥉 Corey Schafer He creates a wide variety of videos on topics that include: Python, Git, Development Environments, Terminal Commands, SQL, Programming Terms, JavaScript, Computer Science Fundamentals, and plenty of other tips and tricks which will help you in your career. 🏆 Most Outstanding Educator in Technology —presented in partnership with Skillsoft A huge thanks to for this award's prizes, which include: Skillsoft 1 Year FREE Access to Percipio to the Winner; and, 6 months Free Access to the runners-up; The winner will also receive a 1 year registration for a .tech domain! And the winner is... 533 votes) 🥇 Sajeetharan Sinnathurai ( Mentor and open source contributor who is empowering and helping every developer in their pretty journey. He works at Microsoft as a Cloud Solution Architect. He is also the top contributor on Stack Overflow from SriLanka and He ranks among the top 10 contributors in the world on Angular, Azure. He contributes his knowledge into open source projects and sharing it with the community by mentoring, creating POCS, running workshops, writing blogs to help make the world a better and more developed place. Runner-ups: 🥈 shishir (339 votes) Through his succinct yet informative pieces, he is able to pack a lot of knowledge into a diverse set of topics. Even in his blog, he covers more such consequential technology and its intersection with society. 🥉 Paul Graham Founder of Y Combinator and author of numerous essential startup essays. 🏆 Top Tech YouTuber —presented in partnership with .TECH Domains A huge thanks to for this award's prizes, which include: .TECH Domains A Free 10-yr domain;1st Runner-up gets Free 5-yr domain ;2nd Runner-up gets a 3-yr domain And the winner is... 5,575 votes) 🥇 Traversy Media ( Traversy Media features the best online web development and programming tutorials for all of the latest web technologies including Node.js, Angular 2, React.js, PHP, Rails, HTML, CSS and much more Runner-ups: 2,524 votes) 🥈 The Net Ninja ( If you're looking to learn Javascript, Flutter, NodeJS, Typescript, or a host of other such cool languages, the Net Ninja's tutorials have you covered 🥉 Ready Dev The best content about python, javascript, django and bots with python. 🏆 Innovative Company of the Year: Artificial Intelligence —presented in partnership with SKILLSOFT A huge thanks to for this award's prize, which includes: Skillsoft 1 Year FREE Access to Percipio to the Winner and 6 months Free Access to the runners-up The winner will also receive a 1 year registration for a .tech domain. And the winner is... (359 votes) 🥇 Behavioral Signals Behavioral Signals' Emotion AI technology proved actual ROI in a commercial deployment, with a large bank, garnering them recognition by Gartner as an example for AI adoption by industry managers. Their AI technology has the ability to gather emotions and behaviors from voice intonation without having to convert speech to text, which is crucial when it comes to user privacy. It's all about HOW you say something and not WHAT you say. Runner-ups: 🥈 Raven Protocol (278 votes) Raven Protocol is working on speeding up AI training. it typically takes about 2-3 weeks to train a 1M image dataset on say AWS. But Raven is building a distribution framework from the ground up that takes this down to 2-3 hours. 🥉 LionbridgeAI Lionbridge provides human-labeled data for hundreds of use cases. 🏆 The Best Analytical Mind in Blockchain —presented in partnership with FLIPSIDE CRYPTO A huge thanks to for this award's prize, which includes: Flipside Crypto Winner shall be featured on 's newsletter, gets an interview, and a blog post. Flipside Crypto The winner will also receive a 1 year registration for a .tech domain. And the winner is... (1,599 votes) 🥇 Akshay Kumar KANDHI Akshay is a blockchain and biometrics researcher and mathematician based out of Paris. He is the Director of R&D at Uniris. Runner-ups: (255 votes) 🥈 Laurent Benayoun With just 1.2k USD as a personal seed investment into the company, Laurent co-founded HedgeTech, a quantitative trading firm focusing on market making for digital assets. 🥉 Paul Payam Almasi Infrastructure Solutions Strategist at Alchemy. The founder of two blockchain analytics platforms. Recognized as a top Bitcoin writer on Medium.com and has served as a cryptocurrency writer for HackerNoon. 🏆 Best Defi Initiative (NeoBanks) —presented in partnership with SUSTANY CAPITAL The winner will receive a 1 year registration for a .tech domain. And the winner is... (44 votes) 🥇 Benjamin Jones Ben is the CEO/CTO and co-founder of Bitwala. Bitwala is the first provider of the crypto bank account of the world. It is a great DeFi initiative to bridge the traditional finance with the DeFi space and offer people a way to reach out to DeFi products with the ease of using bank account. Runner-ups: 🥈 Hassan Khan TodaQ CEO (37 votes) From traditional finance to leading one of the most advanced decentralized platform in the space having the ability to represent any asset cryptographically and most importantly enable its circulation without any dependencies on third parties, no ledger, no miners, no cloud, no thing in the middle means true p2p and subsequently unprecedented economical and technical scaling. All because they are building on TODA protocol with several variations. 🥉 Aviv Gadot, current VP, Engineering 🏆 Hacker Noon Contributor of the Year - BLOCKCHAIN The winner will receive a 1 year registration for a .tech domain. And the winner is... (1,215 votes) 🥇 Vaibhav Saini Entrepreneur | Co-founder @tbc_inc, an MIT CIC incubated startup | Speaker |https://vaibhavsaini.com Runner-ups: 1,011 votes) 🥈 Ishan Pandey ( Lawyer in Making | Contributing in the Blockchain Industry 🥉 Sergey Baloyan Good articles on blockchain 🏆 Hacker Noon Contributor of the Year - DECENTRALIZATION —presented in partnership with SUSTANY CAPITAL The winner will receive a 1 year registration for a .tech domain. And the winner is... (227 votes) 🥇 Adam Boudjemaa An accomplished, results-oriented professional with a comprehensive knowledge of the Crypto / Blockchain industry at a strategic and operational level, and a demonstrable record of success in executing incisive market analyses that inform consistently profitable investments in the space. Runner-ups: 🥈 Erik P.M. Vermeulen (39 votes) Business & finance professor, digital lawyer, restaurant owner, board member & traveler. 🥉 Daniel Jeffries I'm an author, futurist, engineer, systems architect, public speaker and pro blogger. During my twenty years in the tech industry, I’ve designed and implemented a broad range of solutions with Linux, virtualization and containers, first with my own consulting company and now with open source pioneer Red Hat. 🏆 Hacker Noon Contributor of the Year - PROGRAMMING —presented in partnership with UDACITY A huge thanks to for this award's prize, which includes: Udacity A 100% Free Udacity Course on Programming The winner will also receive a 1 year registration for a .tech domain. And the winner is... (72 votes) 🥇 Gajesh S. Naik Allegedly the youngest Programmer with a YouTube channel (Gajesh S Naik) Runner-ups: 🥈 Iman Tumorang (32 votes) Experienced Software Engineer with a demonstrated history of working in the computer software industry more than +3 years experience. Blogger and love to contribute to open-source. Passionate on Startup culture move fast and fix things, with expertise in Software Architecture, Microservices Architecture and Design, API Development and Go (Golang). 🥉 Raphaël Huchet Plus d'infos : https://raph.site 🏆 Hacker Noon Contributor of the Year - WOMEN IN TECH —presented in partnership with Skillsoft A huge thanks to for this award's prizes, which include: Skillsoft 1 Year FREE Access to Percipio to the Winner; and, 6 months Free Access to the runners-up; The winner will also receive a 1 year registration for a .tech domain! And the winner is... (217 votes) 🥇 Lina Survila I am tech PR with a passion for cybersecurity and... fashion. Runner-ups: 143 votes) 🥈 L.B. Lewis ( Selected out of 12k contributors as an "insightful technologist" by Hacker Noon in July 2020. Melinda Lewis is an experienced, strategic marketing professional and best-selling author. She has over eight years' experience in digital marketing and has worked with startups and big brands alike. 🥉 Janani Ravi Ex-google, Janani Ravi is a co-founder at loonycorn, a studio for high quality tech videos. She has created a lot of courses on python, Google cloud platform and machine learning. 🏆 Best Web Monetized Digital Artist —presented in partnership with GRANT FOR THE WEB A huge thanks to for this award's prize, which includes: Grant for the Web A $1000 award for the eventual winner (!!!) The winner will also receive a 1 year registration for a .tech domain. And the winner is... (355 votes) 🥇 Kassjan Smyczek An amazing artist on Coil and Cinnamon Video Runner-ups: 🥈 Connie Digital (133 votes) Connie Digital has produced a wide variety of monetized content, including digital art, music, virtual reality installations, as well as a unique non-fungible crypto token. Over a dozen media outlets have featured his innovative use of digital media and his presence in the virtual space collective known as Metaverse, making him a premier example of what is possible in the new arts economy. 🥉 VESA Mixed media artist VESA innovated his own bodypainting based art form Artevo in 2008, which reached global impact via a Bollywood star project, which altered the foundation of India and Pakistan in 2012. 🏆 VISIT NOONIES.TECH TO VIEW ALL 221 OF HACKER NOON'S 2020 NOONIES AWARD WINNERS Including, but by no means limited to: Startup Blogger of the Year Trending Tech Product of the Year Product Manager of the Year Hacker Noon Contributor of the Year - TECH Hacker Noon Contributor of the Year - PRODUCT MANAGEMENT Hacker Noon Contributor of The Year: CYBERSECURITY NEW Hacker Noon Contributor To Watch Most Exciting Data Storage And Sharing Project Hacker Noon Contributor of the Year - ETHEREUM Hacker Noon Contributor of the Year - CRYPTO Programming Language You Love the Most Hacker Noon Contributor of the Year - RUBY ON RAILS The Most Controversial Writer of the Year Best Use of Tech for Good Mental Health Advocate of the Year Hacker Noon Contributor of the Year - PRIVACY Hacker Noon Contributor of the Year - FUTURE Hacker Noon Contributor of the Year - POLITICS Hacker Noon Contributor of the Year - COVID-19 Hacker Noon Contributor of the Year - ENTREPRENEURSHIP Independent Tech Journalist of the Year Hacker Noon Contributor of the Year - WRITING Slack, Telegram, and/or Discord Channel of the Year Hacker Noon Contributor of the Year - SOCIAL MEDIA Meme of The Pandemic AND . SO MANY MORE A Parting Note RE: Next Year's Noonies The 2021 Awards Nomination period is now less than one year away. Hacker Noon to to ensure you're in the running for one of 🚀 Start submitting your best tech stories the top tag pages on Hacker Noon TODAY these prestigious internet awards next year ! 🤖 / 👾 CREATE YOUR HACKER NOON PROFILE OPEN A NEW DRAFT

2020 Noonies Awards: Official Winners of The Internet Now Declared 

is the CEO of , speaker, investor and dedicated voice for conversational AI and emotion AI and Noonies nominee. Rana Gujral Behavioral Signals In with the Hacker Noon community, he shared his views on Ask Me Anything session 1. Challenging the moral foundations of society with self-aware AI "It will raise many questions around how we treat our self-aware robot counterparts? How should we interact? What duties (moral) do we have towards them? What about rights? Will morality promote fostering or thwarting that emerging intelligence?" 2. A secret sauce of most profitable companies in terms of investment "Those which are solving for a well-defined problem AND have the abilities (skills, access, resources) to execute on that vision." 3. On teaching AI to understand humans emotion while engaging in interaction "Overall, more than 600 features are used to represent an utterance in the recording in the domain of emotions and behaviors. Based on these features we then train a set of deep models to identify various emotional and behavioral states." The emotion AI’s major breakthrough of the last decade 4. 5. Teaching a robot to differentiate emotions or human to automate work processes? "With these two trends, with machines becoming more and more intelligent, and with us relying more and more on the machines, it almost becomes imperative that a super-intelligent machine that we are dependent on or we are interacting with has emotional intelligence." Read the full AMA. Vote for Rana in , , Hacker Noon Contributor of the Year: CHATBOTS Hacker Noon Contributor of the Year: ARTIFICIAL INTELLIGENCE ML Writer of the Year.

Emotion AI Discussion with Rana Gujral via Hacker Noon AMA

(image from bloomberg.com/) Like my articles? Feel free to vote for me as ML Writer of the year here . It takes no political analysis expertise to tell that public opinion is much influenced by and the politicians in public. In this article we have used 30 videos from the two politicians, relatively uniformly spread from January 2020 until today (September 2020), to demonstrate how speech analytics can be used to extract valuable conclusions from such data. what how speak Our goal is to illustrate WHAT and HOW the two candidates have been speaking about during the last year Method : 35 videos from speeches from both candidates (around 2 speeches per month on average) have been used to extract our analytics. Data data were retrieved through Automatic Speech Recognition (ASR). This information will help us to visualize the two candidates talked about. ASR, of course is not totally error-free, the ASR model used in this particular use-case have a word-error-rate around 25% on the particular data. However, in this article we have used simple aggregates to analyze our final texts (such as word clouds), and so the error of this final representation should be much lower. Text what The raw data from the speeches,  were analyzed in terms of the speakers and using . In particular, we focused on the speech emotional strength (arousal) and the speech emotional positivity (also known as valence). This second level of analysis will provide a metric on the two candidates have spoken during that period. audio emotions behaviors Behavioral Signals Oliver API how What have they been talking about? First, lets see how the topics the two candidates have discussed can be visualized in a simple across time. word cloud This is Trump's word cloud for some of the months in the selected period (January - September 2020): And this is Biden's: Some conclusions directly drawn from the diagrams above: In the beginning of the year, both candidates' speeches contained a lot of "thank", "god", "bless", "proud" and "folks", probably the context was more "festive" in this period... During the first wave of the crisis in the States (Feb, Mar), Trump used the words "countries" (in the context of comparing USA deaths to other countries statistics), "flu", "coronavirus", "recovered" and "decisions", while Biden focused on words like "crisis", "health", "workers", "public" and "care". COVID-19 After on 25th of May, and during the demonstrations that followed, the two candidates' speeches in June where like this: Biden talked about "justice", "King" (note: Martin Luther obviously), "community", "black", "nation", "police", "violence" and "protest", while Trump about "action", "anarchist", "destruction", "enforcement", "state", "military", "peaceful", "law", "protect" and "property". Floyd's death During the last 3-4 months and , both candidates use their opponents names but Trump uses "Joe" and "Biden" much more frequently than Biden uses "Trump", "Donald" and "President". Also, during that period, Trump uses the words "vaccine", "economic", "depression", "job", "radical", "left" (most of these usually connected to Biden himself). Biden uses "school", "climate", "community", "movement", "job", "promise", "opportunity" and "care". as the elections day is approaching How did they sound? Now let's focus a bit on their tone of voice. We have initially focused on two metrics, namely the . Strength and positivity are two widely used emotional dimensions used in speech emotion recognition (SER) and psychology. For example, according to this emotional representation, anger is strong and negative, sadness is weak and negative and happiness is strong and positive. As described above, we have extracted these two emotional measures by combining outputs. emotional strength and positivity Oliver API Below is the two candidates' emotional graph. strength In both cases, candidates' speeches were more "energetic" in the beginning of the year. Trump's speeches are also becoming with higher emotional energy during the last two months. Also, Biden's emotional energy was minimized during June, which makes sense, if we consider his speeches about Floyd's death (where sadness was the most dominant emotional state). is shown in the next graph: Positivity Again, Biden has the most negative emotions in June and July, while Trump is neutral from March to July and highly positive in the beginning of the year and during the last two months. The graphs above show of emotional strength and positivity. So an extreme case where a candidate would be 50% negative and 50% positive in the same recording would be shown as neutral. For this reason, we may need a third diagram to describe the speakers' emotional state: . This is a measure of emotional fluctuations in the same speech (e.g. going from negative to positive emotion or from angry to happy). The following diagram shows the emotional diversity of the two candidates for all months: averages emotional diversity If we exclude February (i.e. only in 11% of the months), , while for 3 out of the 9 months (34%)  the two candidates had both almost zero diversity. in 5 out of the 9 months (55%) Trump had a significantly higher emotional diversity

You Are Totally Going To Love My 2020 USA Candidates' Speeches Analysis

Audio Handling Basics: Process Audio Files In Command-Line or Python

Conceptos básicos de manejo de audio: Procesar archivos de audio en la línea de comandos o Python

The Ultimate Toolbox Of ML Startups

How to use machine learning to color your room lighting, based on the emotions behind the music you are listening (Python code available here ) Emotion is a very important attribute of audio signals, either if it is speech or music. In the case of speech it can be used to enhance  speech analytics  by emotional and behavioral content. For music, it also makes a more representative and detailed description of the musical content, in a way that song retrieval and recommendation applications can be . Therefore, recognizing emotions directly from the audio information has gained increased interest during the last years. In the demo described in this article I am trying to show how estimating emotions in musical signals can be performed using Python and simple ML models, and then, for each music signal, how to  map the estimated emotions to colors to be used in the lighting of the room where the music is played. For changing the room's lighting a smart lighting device is obviously required: in this demo we are using the Yeelight color bulb emotion-aware Let’s start with a quick description of how we solve the “music to emotions” task. Audio data and ground truth We first need to train a machine learning model that will be able to map audio features to emotional classes. For this demo, a set of 5K music samples (songs) has been compiled. Emotional ground-truth has been gathered through the and in particular by using (a Python library to use in the Spotify API). The Spotify API provides access to several song metadata, including the emotional and . Spotify API Spotipy valence energy Valence and energy (usually the term “Arousal” is used in the bibliography especially for speech emotion recognition, but here we have selected to use the term energy to be aligned with the Spotify attributes) are dimensional representations of the emotional states, initially adopted by phycologists as an alternative to distinct emotions (sadness, happiness, etc). They have become common in emotion recognition. Valence is related to the positivity (or negativity) of the emotional state and energy (or arousal) is the strength of the respective emotion. The Spotify valence and energy values are continuous in the (0, 1) range (valence ranging from negative to positive and energy from weak to strong). In this demo, we have selected to sample the valence and energy values to three distinct classes: valence to negative, neutral and positive and energy to weak, normal and strong. Towards this end, different thresholds have been used to maintain a relative class balance for both classification tasks. In addition, as shown in the experiments below, initial audio signals have been downsampled to 8K, 16K and 32K. As we show later, we have selected 8K as the higher sampling frequencies do not significantly improve the performance. Also, from each song, a fix-sized segment has been used to extract features and train the respective models. The segment size has varied for the experiments presented below, but for this particular demo a default segment size of 5 seconds has been selected, because this has been considered to be a rational segment size upon which music emotion predictions are made: longer segment sizes would mean the “user” would have to wait for a longer time for emotional predictions to be returned. On the other hand, as we will see below, shorter segment sizes lead to higher emotion recognition error rates. To sum up, our dataset consists of 5K music segments (from 5K songs), organized in 3 emotion energy classes (low, normal, high) and 3 valence classes (negative, neutral, positive) taken at various sampling frequencies and various segment sizes. The final selected parameters for this demo are 8K sampling frequency and a segment size of 5 seconds. These samples are then used to train two models (for energy and valence) Features and Classifier Training The Python library has been used for extracting hand-crafted audio features and for training the respective segment-level audio classifiers. A set of almost 130 statistics computed over short-term time-domain and frequency-domain audio features are extracted per segment. So For classification we have selected SVMs with RBF kernels (spectrograms and CNNs could be used for better performance, but this would require more data for training, and more computational demands both at training and run-time of the final demo) pyAudioAnalysis each audio segment is represented by a 130-dimension feature vector. emotion recognition performance results Music During  training and parameter tuning a typical cross-validation procedure has been followed: data has been split into train and test subsets and performance measures have been evaluated for each random train/test split. The overall confusion matrices for both classification tasks are shown below. Rows represent ground truth classes and columns predictions. E.g. 5.69% of the data has been misclassified as "neutral", while their true energy label was "low". As expected, emotional energy is an "easier" classification task, with an overall F1 close to 70%, while F1 for valence is around 55%. In both cases, the possibility of an "extreme" error (e.g. misclassification of high energy as low) is very low. How accurate can the two classifiers be for the selected parameters (5 sec length, 8KHz sampling)? (energy, F1 = 67%) (valence, F1 = 55%) The obvious answer is that higher sampling frequencies would lead to better overall performance. However, experimental results with 8K, 16K and 32K sampling frequencies have proven that the average (for many segment lengths from 5 to 60 seconds) performance does not significantly change. This indicates using 8K for this particular demo, as the aforementioned performance boosting, does not justify the computational cost (32K means 4 times more feature extraction computations that 8K signals). So the short answer is that the sampling frequency does not significantly affect the overall prediction accuracy. Does signal quality (sampling rate) matter? In particular, and compared to the 8K signal quality, overall performance is just 0.5% better for 16K and 2% for 32K. It is obvious that, the longer the segments the more the classifier would have to estimate music emotion. But how does segment length affect overall F1 measure? The results in the following figure prove that overall performance (F1) for energy can even be above 70% for 30-second segments, while valence seems to asymptotically reach 60%. in our case, where we want to automatically control our color bulbs based on the estimated emotion, a rational delay would be less than 10 seconds (as explained before, 5 is the selected segment length). How does segment length affect performance? information Of course, the segment length cannot be as high as possible and it is limited by the application: Emotion to color So we have shown above that we can build two models for predicting emotional energy and valence in 5-second music segments with an accuracy around 70% and 55% respectively. How can these two values (energy and valence) be used to change the color of the room's lighting the music is played in? Studies have proven that there is a strong association between colors and emotions derived from musical content (e.g. ). In general, emotion has been associated with colors in various contexts such as human-computer-interaction, phycology and marketing. In this demo, we have just followed some common admissions such as that . However, this mapping is not the result of a deep dive into the phycological factors behind emotional content and color representation. For that reason, the demo presented in this article is 100% configurable with regards to the emotions to color mappings (i.e. the user/developer can change the mappings of emotions to colors very easily). this publication yellow usually represents joy, blue sadness, red anger/alert and green calmness To define a continuous 2D valence-energy plane where each point is mapped to a single color, we define a discrete mapping of points to colors based on the aforementioned assumptions. As an example, anger, i.e. point (-0.5 , 0.5) in the valence, energy plane is mapped to red and happiness (point (0.5, 0.5)) to yellow, etc. All intermediate points are assigned as a linear combination of the basic color mappings, based on their Euclidean distance from the points of reference. The result is the continuous 2D valence-energy color plane shown below. The horizontal axis varies in the negative - positive range (valence), while the vertical axis corresponds to emotional energy (weak to strong). The particular colormap is the default mapping of (valence, energy) value pairs to color, based on a set of simple configurable rules, as described above (Note: In the Python repo of this article, this can be changed by editing the "emo_map" dictionary in main.py). Integration with the Yeelight Bulbs The presented demo functions as follow: for each 5-sec window recorded, the (valence, energy) values are predicted using the models described above. Then, the predicted point is mapped to the respective color and visualized in a simple plot on the computer that the demo is running. If the user provides one or more IPs of Yeelight Bulbs, the Yeelight Python library ( ) is used to set the colors of the Bulbs based on the aforementioned predictions. https://yeelight.readthedocs.io/en/latest/ Video demo Implementation The demo described in this article is available at this github repo: . It mostly uses for feature extraction and classifier training/testing. It also includes pretrained energy and valence models for music signals. https://github.com/tyiannak/color_your_music_mood pyAudioAnalysis About me I am the Director of ML at , where my work focuses on building algorithms that recognise emotions and behaviors based on audio information. I also teach multimodal information processing in a and master program in Athens, Greece. behavioralsignals.com Data Science AI

How to Use Machine Learning to Color Your Lighting Based on Music Mood

It can feel at times like we live in a science fiction future. We hold the whole of human knowledge in palm-sized devices that are constantly connected to the Internet. We speak to our computers and they respond with seemingly intelligent feedback. But while the hardware that powers our lives has advanced at rapid speed in the last three decades, voice assistant technology still relies heavily on the same human input that traditional software programs have for much of that time. Amazon Alexa, for example, alone through June of this year. These are individually programmed interactions that users can have with their Alexa devices – from ordering products to checking the weather or playing a trivia game. had 63,215 skills in the United States But for Alexa to become a true digital assistant, the platform – and those like it in other devices – needs to be more proactive. This is where behavioral intent prediction comes in, utilizing machine learning to evaluate and predict user behaviors based on thousands of inputs. The result will be a much more human-like interaction – with a device that can predict what a consumer needs and when they need it, much the same as a human assistant. For companies, this will lead to a boom in data insights that further enhance targeting, personalization, and the likelihood of a sale. What is Behavioral Intent Prediction? The ability to predict the behavior of a consumer is a holy grail to many corporations. Billions of dollars are spent annually on market research, behavioral analysis, and new technologies to deliver smarter advertising to users. So, it’s no wonder we are starting to see an increase in the sophistication of our voice-activated devices – these are consumer applications after all. In 1980, for predicting behavior in consumers based on intentions. While the existing models that had long been used by marketers focused on attitude measurements developed by those marketers, the new model is designed to evaluate the subjective intent of an individual to perform a specific behavior. In short, there is a scientific basis for predicting what someone will do based on a number of variables. Paul Warshaw summarized a new model Fast forward nearly forty years and developers are using a similar approach to “teach” VUI’s like Alexa and Siri to learn more about their users and respond in kind. Of course, there are many challenges to successfully doing this as well. The sheer volume of data that needs to be collected, cataloged and labeled before it can be input into the system is extensive. Amazon, for example, spends a considerable amount of money having thousands of hours of audio annotated each day to help the system better understand key elements based on the content of user speech. How Voice Interfaces Learn to Read Intent One of the biggest challenges with voice systems in their early iterations was how specific you needed to be. Everyone has attempted to trigger a command with their phone or Echo device and found that they did not use the right combination of words to trigger the action. These devices have been improved substantially and now attempt to determine, from context, what the user is asking, even if the specific language that triggers a skill is not used. Colloquialisms and variations on questions allow users to ask, “What’s it like outside?” or “Should I wear my coat?” instead of specific inquiries like “What is the weather in Chicago today?” Behavioral Intent Prediction goes beyond the reading of context in vague questions, though. It allows these systems to start evaluating key elements of how a user interfaces with the system each day. This is a form of skill that allows Alexa to try and figure out what someone means based on location, time of day, or recent activity. most evident in “Hunches”, For homes that have sensors installed or for more advanced applications that interface with IoT devices, this allows for some creative implementations of voice control. For example, you might say “Alexa, play some music,” and the device would be able to intuit which connected device to play the music on, and what volume to set based on the time of day and the typical volume you choose. The number of times that users have to reframe questions repeatedly to get the response they expect and desire is decreasing as these interfaces get smarter and better able to evaluate intent and respond in kind. Predictive analytics go well beyond what a company might see in a survey or market research study and analyzes every element of a phone call, VUI interaction, or other recorded discussion. This allows developers and marketers alike to evaluate the root cause of a conversation, why someone’s mood changes during such a conversation (an invaluable resource in customer service), and much more. The result is a better user experience that caters itself to the user, and more actionable data for companies. How Emotion AI Supplements Behavioral Intent Prediction? One of the many barriers to a predictive model in voice assistant technology was the lack of context. Devices could hear commands and respond, and to some degree evaluate the specific words being spoken, but only with a more advanced approach to the context of the words and how they are spoken can the next step be taken. Emotion AI is capable of evaluating several elements of the user beyond their words. For example, it can take into account the regional dialect of the user, the micro-cues that indicate a specific emotion that might influence how and why they are saying something. On a small scale, Alexa is now able to recognize when someone is whispering and whisper back – a godsend for parents trying to check the time or the weather while holding a sleeping baby. Now imagine when the system could anticipate a mood entirely based on how something was said and respond accordingly, not only with the right content but in a way that is catered to those emotions. Behavioral Intent Prediciton is only one example of an emerging technology that is bridging the gap between human and machine. Advancements such as these are inevitable based on the current trajectory of implementation and discovery. Rather than be fearful or doubtful of their abilities, take this as an opportunity to explore and engage in the understanding of how technology will advance humankind.

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