Arria is a Natural Language Generation company that replicates the human process of expertly analyzing and communicating data insights. We caught up with their CTO, Neil Burnett, to learn more about how Arria's technology goes beyond the standard rules-based NLP approach, as well as how the technology develops and grows once it's placed in the hands of the consumer. Neil explains the huge opportunity within NLG, and how solving for seamless language based communication between humans and machines will result in increased trust and widespread adoption in AI/ML technologies.

Traditional LiDAR systems require moving parts to operate, making them less cost-effective, robust, and safe. Cibby Pulikkaseril is the Founder and CTO of Baraja, a company that has reinvented LiDAR for self-driving vehicles by using a color-changing laser routed by a prism. After his Ph.D. in lasers and fiber optic communications, Cibby got a job at a telecom equipment company, and that is when he discovered that a laser used in DWDM networks could be used to reinvent LiDAR. By joining this conversation, you’ll hear exactly how Baraja’s LiDAR technology works and what this means for the future of autonomous vehicles. Cibby also talks about some of the upcoming challenges we will face in the world of self-driving cars and the solutions his innovation offers. Furthermore, Cibby explains what spectrum scan LiDAR can offer the field of robotics more broadly. Key Points From This Episode:Cibby’s background in fiber optic communications and what led him to found Baraja.Realizing that a laser used in DWDM networks could be applied to LiDAR. Why Cibby decided that autonomous vehicles (AVs) were a good application for the laser.How the laser used by Baraja can steer a LiDAR beam without any moving parts thus making the system cheaper.Velodyne’s contributions and other innovations in the LiDAR space.A description of how the spectrum scan LiDAR works using a color-changing laser routed by a prism.The infinite resolution made possible by colored light and how AI will make use of it.Hazards around the over-proliferation of conventional LiDAR laser and how Baraja’s tech gets past this.Other challenges Cibby predicts will exist once AVs start to proliferate.How Baraja’s solid-state LiDAR technology will advance other fields of robotics.Cibby’s level of involvement in the coding and R&D at Baraja as the CTO.Technical areas that the Baraja team is researching and developing such as homodyne detection.Advice from Cibby for how to innovate in the already cutting-edge space of computer vision. Tweetables:“We started to think, what else could we do with it. The insight was that if we could get the laser light out of the fiber and into free space, then we could start doing LiDAR.” — Cibby Pulikkaseril [0:01:23]“We were excited by this idea that there was going to be a change in the future of mobility and we can be a part of that wave.” — Cibby Pulikkaseril [0:02:13]“We are the inventors of what we call spectrum scan LiDAR that is harnessing the natural phenomenon of the color of light to be able to steer a beam without any moving parts.” — Cibby Pulikkaseril [0:03:37]“We had this insight which is that if you can change the color of light very rapidly, by coupling that into prism-like optics, this can route the wavelengths based on the color and so you can steer a beam without any moving parts.” — Cibby Pulikkaseril [0:03:57]Links Mentioned in Today’s Episode:Cibby Pulikkaseril on LinkedInBaraja 

Academic turned entrepreneur Michel Valstar joins How AI Happens to explain how his behaviomedics company, Blueskeye AI, prioritizes building trust with their users. Much of the approach features data opt-ins and on-device processing, which necessarily results in less data collection. Michel explains how his team is able to continue gleaning meaningful insight from smaller portions of data than your average AI practitioner is used to. Michel Valstar on LinkedInBlueskeye AI

Joining us today is Senior Director at Facebook AI, Manohar Paluri. Mano discusses the biggest challenges facing the field of computer vision, and the commonalities and differences between first and third-person perception. Manohar dives into the complexity of detecting first-person perception, and how to overcome the privacy and ethical issues of egocentric technology. Manohar breaks down the mechanism underlying AI based on decision trees compared to those based on real-world data, and how they result in two different ideals: transparency or accuracy. Key Points From This Episode:Talking to Manohar Paluri, his background in IT, and how he wound up at Facebook AI. Manohar's advice on the pros and cons of doing a Ph.D.Why computer vision is so complex for machines but so simple for humans. Why the term “computer vision” is not a limiting definition in terms of the sensors used.How computer vision and perception differ. The two problems facing computer vision: recognizing entities and augmenting perception. Personalized data; generalized learning ability; and adaptability: the three problems that are responsible for the low number of entities that computer vision recognizes.Managing the direction Manohar's organization is going: egocentric vision, predicting the impact of modeling, and finding the balance between transparency and accuracy. Find out what the differences are between first- and third-person perception: intention, positioning, and long-form reasoning. The similarity between first- and third-person perception: both are trying to understand the world.Which sensors are required to predict intention: gaze and hand-object-interaction. What the privacy and ethical issues are with regard to egocentric technologies. Why Manohar believes striking a balance between accuracy and transparency will set the standard. The three prospects in AI that excite Manohar the most: the next computing platform, bringing different modalities together, and improved access to technology.  Tweetables:“What I tell many of the new graduates when they come and ask me about ‘Should I do my Ph.D. or not?’ I tell them that ‘You’re asking the wrong question’. Because it doesn’t matter whether you do a Ph.D. or you don’t do a Ph.D., the path and the journey is going to be as long for anybody to take you seriously on the research side.” — Manohar Paluri [0:02:40]“Just to give you a sense, there are billions of entities in the world. The best of the computer vision systems today can recognize in the order of tens of thousands or hundreds of thousands, not even a million. So abandoning the problem of core computer vision and jumping into perception would be a mistake in my opinion. There is a lot of work we still need to do in making machines understand this billion entity taxonomy.” — Manohar Paluri [0:11:33]“We are in the research part of the organization, so whatever we are doing, it’s not like we are building something to launch over the next few months or a year, we are trying to ask ourselves how does the world look like three, five, ten years from now and what are the technological problems?” — Manohar Paluri [0:20:00]“So my hope is, once you set a standard on transparency while maintaining the accuracy, it will be very hard for anybody to justify why they would not use such a model compared to a more black-box model for a little bit more gain in accuracy.” — Manohar Paluri [0:32:55]Links Mentioned in Today’s Episode:Manohar Paluri on LinkedInFacebook AI Research WebsiteFacebook AI Website: Ego4D

In recent years, the focus of AI developers has been to implement technologies that replace basic human labor. Talking to us today about why this is the wrong application for AI (right now), is Katya Klinova, the Head of AI, Labor, and the Economy at The Partnership on AI. Tune in to find out why replacing human labor doesn't benefit the whole of humanity, and what our focus should be instead.  We delve into the threat of "so-so technologies" and what the developer's role should be in approaching ethical vendors and looking after the workers supplying them with data. Join us to find out more about how AI can be used to better the whole of society if there’s a shift in the field’s current aims. Key Points From This Episode:An introduction to Katya Klinova, Head of Al, Labor and the Economy at The Partnership on AI.How her expectations of the world after her undergraduate degree shaped her.Pursuing a degree in economics to understand how AI impacts labor and economics.The role of The Partnership on AI in dissipating technological gains.Who is impacted when AI is introduced to a market: the consumers and the workers.How different companies are deficient in the ways they benefit everyone. Find out what the “threat of so-so technology” is.Should people become shareholders in AI technology that they helped to train?How capitalism incentivizes “so-so technologies”. The role of developers in selecting vendors and responsible sourcing. Why it's important to realize that data labelers are employees and not just numbers.Shifting the focus of AI from automation to complementarity. Why now is not the time to be replacing human labor.  Tweetables:“Creating AI that benefits all is actually a very large commitment and a statement, and I don't think many companies have really realized or thought through what they're actually saying in the economic terms when they're subscribing to something like that.” — @klinovakatya [0:09:45] "It’s not that you want to avoid all kinds of automation, no matter what. Automation, at the end of the day, has been the force that lifted living conditions and incomes around the world, and has been around for much longer than AI." — @klinovakatya [0:11:28] “We compensate people for the task or for their time, but we are not necessarily compensating them for the data that they generate that we use to train models that can displace their jobs in the future.” — @klinovakatya [0:14:49] "Might we be automating too much for the kind of labor market needs that we have right now?" — @klinovakatya [0:23:14] ”It’s not the time to eliminate all of the jobs that we possibly can. It’s not the time to create machines that can match humans in everything that they do, but that’s what we are doing.” — @klinovakatya [0:24:50] Links Mentioned in Today’s Episode:Katya Klinova on LinkedIn"Automation and New Tasks: How Technology Displaces and Reinstates Labor"The Partnership on AI: Responsible Sourcing

In this episode, we talk to Stefan Scherer (CTO of Embodied) about why he decided to focus on the more nuanced challenge of developing children’s social-emotional skills. Stefan takes us through how encouraging children to mentor Moxie (a friendly robot) through social interaction helps them develop their interpersonal relationships. We dive into the relevance of scripted versus unscripted conversation in different AI technologies, and how Embodied taught Moxie to define abstract concepts such as "kindness". Key Points From This Episode:Welcome to Stefan Scherer, CTO of Embodied and lead researcher and developer of Embodied's SocialX™ technology, Moxie.The goal of Embodied: using a natural mode of communication to support children’s social development. Mentoring Moxie: how Moxie teaches children social-emotional learning without being a teacher. Why Stefan and Embodied focused on the challenge of social-emotional skills, not STEM. Developing a technology that captures the infinite answers to social-emotional questions: using neural networks and sentiment analysis.How using Few-shot learning reduced the amount of data needed to train Moxie.Why it's important to make the transition between freer- and scripted conversations seamless.How the percentage of scripted versus non-scripted conversation differs based on the context of the technology.  Discover how Moxie adapts to children’s changing needs and desires. How Moxie as a springboard in teaching children to form long-term relationships. The hardware behind Moxie: the ethical considerations around home devices, and data protection.Why Moxie looks the way it does: making it affordable. Tweetables:“Human behavior is very complex, and it gives us a window into our soul. We can understand so much more than just language from human behavior, we can understand an individual's wellbeing and their abilities to communicate with others.” — Stefan Scherer [0:01:04]"It is not sufficient to work on the easy challenges at first and then expand from there. No, as a startup you have to tackle the hard ones first because that's where you set yourself apart from the rest." — Stefan Scherer [0:04:53]“Moxie comes into the world of the child with the mission to basically learn how to be a good friend to humans. And Moxie puts the child into this position of teaching Moxie about how to do that.” — Stefan Scherer [0:17:40]"One of the most important aspects of Moxie is that Moxie doesn't serve as the destination, Moxie is really a springboard into life." — Stefan Scherer [0:18:29]“We did not want to overengineer Moxie, we really wanted to basically afford the ability to have a conversation, to be able to multimodally interact, and yet be as frugal with the amount of concepts that we added or the amount of capabilities that we added.” — Stefan Scherer [0:27:17]Links Mentioned in Today’s Episode:See Moxie in ActionStefan Scherer on LinkedInEmbodied Website

Today we talk to Celtra Founder and CPO, Matevž Klanjšek, about how AI can be used to accelerate creativity, and what would happen if it eventually replaces humans in the creative space. We discuss the limitations of the tools currently available, why Matevž isn’t interested in teaching AI variance, and how humans and AI need to work together in advertising. Tune in to hear what the future of advertising looks like, and why the human-AI feedback loop is essential. Matevž tells us about the bizarre adverts he’s seen AI produce, and talks us through the evolution of human creativity: from paintings to photographs, and how humans stay relevant when we invent something new. Key Points From This Episode:An introduction to the founder and CPO of Celtra, Matevž Klanjšek.Where the idea of using AI in advertising came from.How Celtra technology helps creatives scale their media, accelerating creativity.Why AI is the right tool for the job.Two dangers of using AI in advertising: impacting the communication strategy, and losing uniqueness.Can you teach AI variance?Why it’s important to leave space for human error.Humanity in advertising: why brands are trying to be more human.What a collaboration between humans and AI looks like.How human genius lies in building communication strategies.The surprising results when AI tries to create adverts.Playing with generative design: how AI can inspire humans.Why AI won’t replace humans in the future.Tweetables:“It just makes sense to automate [repetitive tasks] as much as possible, and remove that from the equation, let human genius think about big ideas and communication strategies, creativity and so on.” — @hyperhandsome [0:03:47]“I think on all of the levels, across the creative process, we always try to have humans involved. It’s almost like a basic principle.” — @hyperhandsome [0:14:47]“So that’s the nice thing, actually, perhaps using pretty advanced AI to really inspire creativity in humans instead of replacing it. It’s kind of beautiful in a way.” — @hyperhandsome [0:17:24]"I think the whole point of advertising, and humanity in general is precisely to be always different, to invent new things." — @hyperhandsome [0:18:12]“I think technology always gets to a point where it can perfectly imitate, and do it better than humans can, but then we invent something new.” — @hyperhandsome [0:19:52]Links Mentioned in Today’s Episode:Matevž Klanjšek on LinkedInCeltra Technologies

Joining us today is Dr. Bill Porto, Redpoint Senior Analytics Engineer and storied AI researcher, academic, and developer. Bill shares all his current projects,  including pattern recognition and optimization models, and he reveals what it was like to work with the father of Evolutionary Programming, Dr. Larry Fogel. We touch on a new definition for computational intelligence, and talk about where evolutionary programming is in use today, before exploring the fact that evolution is not simply survival of the fittest, but increases variance through retaining less perfect fits. What's more, we define evolution as adaptation in a dynamic environment.  Key Points From This Episode: Be introduced to today’s guest, Bill Porto, Redpoint Senior Analytics Engineer.How he entered the industry, his background in applied math, and how he ended up in his current role.The subjects he is working on now: pattern recognition and optimization models, personalized recommendation systems and business process optimization.What it was like to work with Larry Fogel, a polymath in the true sense of the word.How computational intelligence is just taking cues from nature.Where evolutionary programming is in use today: commercial and government organizations, transport, the pharmaceutical industry, and more.Why evolution is not really survival of the fittest, but increases variance by retaining more solutions.How evolutionary processes require noise and how we should control what kind of noise it accesses.What evolution is all about: adaptation in a dynamic environment.Why having solutions that are medium fits can help you find exactly the right one.How there is no single algorithm for all optimization problems.Why, if you spend a lot of time getting a perfect solution, it may be stale by the time you implement it.How important it is to prioritize customer satisfaction and optimize human resourcesWhy considering different goals and attaching different weights to them is so important.Why a hybrid approach is good engineering practice as is using the best tool for the job.How customer acquisition is not the same thing as customer retention.Non-discrete, asymmetric bowl functions as a way to create solutions.Scalability as a feature of the current landscape that enables us to tackle large problems.Why continual learning is such a powerful approach. Tweetables:“Computational intelligence is just taking cues from nature. And nature adaptively learns using iterative evaluation selection. So why not put that into an application on a computer?” — Bill Porto [0:04:18]“It’s not really survival of the fittest, that’s the common moniker for it, in reality evolution favors the solutions that are most fit, but it tends to retain a number of less fit solutions, and one of the benefits of that is it increases the variance in the number of solutions.” — Bill Porto [0:07:20]“If you spend a lot of time getting a perfect solution, by the time you have it, it very well may be stale.” — Bill Porto [0:15:17] Links Mentioned in Today’s Episode:Redpoint GlobalBill Porto on LinkedIn

CEO Shahan Lilja joins to explain how Mavenoid is able to deploy custom chat bots in a matter of minutes, the processes by which these tools get better over time, and how the ability to automatically turn technical expertise into an algorithm that can be utilized at scale is amplifying human intelligence. 

Rob Carpenter is the founder and CEO of Valyant AI, which is on a journey to solve the complex problem of conversational AI in the food service industry. In today’s episode, Rob explains the three main components of AI speech processing (and the challenges that arise at each of these nodes), how conversational AI has the capacity to improve conditions for human workers in the food service industry, and what this technology is going to be like in the future. After this episode, you’ll understand the importance of being more thoughtful about how you communicate your next burger and fries order to a conversational AI system. Key Points From This Episode:Rob’s early interest in entrepreneurship. The original idea that Rob wanted to center his company around, and why it didn’t work out that way. What Valyant AI does. Long term goals that Rob has for his company. Challenges of conversational AI in the food service industry.  Benefits of being an industry newcomer. The “three amigos” of speech processing.Examples of customer statements which highlight why a natural language processor is such a vital part of AI speech processing.How people need to learn to communicate with AI systems. The deficit of employees in the restaurant industry.Ways that conversational AI improves working conditions for food service industry employees. Progress that we have made as a society as a result of innovation. What we can expect from conversational AI in the next five to ten years. Tweetables:“I thought the hologram was the hard part and that the conversational AI was solved, but it was basically the inverse of that.” — Rob Carpenter [0:06:47]“There’s benefits when you get into a new industry or technology not knowing the problems, because you don’t know what your limitations are. I think a lot of times that frees you up to be more creative and innovative.” — Rob Carpenter [0:08:38]“If I was to postulate where things would end up, I’d say it’s probably a 90/10. 90% is that the technology has to be better, and keep getting better. 90% of everything needs to be handled by the AI. The other 10%, people need to be more thoughtful when they communicate with these systems.” — Rob Carpenter [0:17:22]“There’s 1.7 million unfilled positions in the restaurant industry right now. 1 in 6 of every position available right now is in restaurants.” — Rob Carpenter [0:20:26]“Innovation is not only built into economies, but it’s essential for their health and long-term safety.” — Rob Carpenter [0:23:28]Links Mentioned in Today’s Episode:Rob Carpenter on LinkedInValyant AI