Podcasts about Natural language processing

I, Stewart Alsop, welcomed Alex Levin, CEO and co-founder of Regal, to this episode of the Crazy Wisdom Podcast to discuss the fascinating world of AI phone agents. Alex shared some incredible insights into how AI is already transforming customer interactions and what the future holds for company agents, machine-to-machine communication, and even the nature of knowledge itself.Check out this GPT we trained on the conversation!Timestamps00:29 Alex Levin shares that people are often more honest with AI agents than human agents, especially regarding payments.02:41 The surprising persistence of voice as a preferred channel for customer interaction, and how AI is set to revolutionize it.05:15 Discussion of the three types of AI agents: personal, work, and company agents, and how conversational AI will become the main interface with brands.07:12 Exploring the shift to machine-to-machine interactions and how AI changes what knowledge humans need versus what machines need.10:56 The looming challenge of centralization versus decentralization in AI, and how Americans often prioritize experience over privacy.14:11 Alex explains how tokenized data can offer personalized experiences without compromising specific individual privacy.25:44 Voice is predicted to become the primary way we interact with brands and technology due to its naturalness and efficiency.33:21 Why AI agents are easier to implement in contact centers due to different entropy compared to typical software.38:13 How Regal ensures AI agents stay on script and avoid "hallucinations" by proper training and guardrails.46:11 The technical challenges in replicating human conversational latency and nuances in AI voice interactions.Key InsightsAI Elicits HonestyPeople tend to be more forthright with AI agents, particularly in financially sensitive situations like discussing overdue payments. Alex speculates this is because individuals may feel less judged by an AI, leading to more truthful disclosures compared to interactions with human agents.Voice is King, AI is its HeirDespite predictions of its decline, voice remains a dominant channel for customer interactions. Alex believes that within three to five years, AI will handle as much as 90% of these voice interactions, transforming customer service with its efficiency and availability.The Rise of Company AgentsThe primary interface with most brands is expected to shift from websites and apps to conversational AI agents. This is because voice is a more natural, faster, and emotive way for humans to interact, a behavior already seen in younger generations.Machine-to-Machine FutureWe're moving towards a world where AI agents representing companies will interact directly with AI agents representing consumers. This "machine-to-machine" (M2M) paradigm will redefine commerce and the nature of how businesses and customers engage.Ontology of KnowledgeAs AI systems process vast amounts of information, creating a clear "ontology of knowledge" becomes crucial. This means structuring and categorizing information so AI can understand the context and user's underlying intent, rather than just processing raw data.Tokenized Data for PrivacyA potential solution to privacy concerns is "tokenized data." Instead of providing AI with specific personal details, users could share generalized tokens (e.g., "high-intent buyer in 30s") that allow for personalized experiences without revealing sensitive, identifiable information.AI Highlights Human InconsistenciesImplementing AI often brings to light existing inconsistencies or unacknowledged issues within a company. For instance, AI might reveal discrepancies between official scripts and how top-performing human agents actually communicate, forcing companies to address these differences.Influence as a Key Human SkillIn a future increasingly shaped by AI, Sam Altman (via Alex) suggests that the ability to "influence" others will be a paramount human skill. This uniquely human trait will be vital, whether for interacting with other people or for guiding and shaping AI systems.Contact Information*   Regal AI: regal.ai*   Email: hello@regal.ai*   LinkedIn: www.linkedin.com/in/alexlevin1/

In this episode of AI + a16z, Sesame Cofounder and CTO Ankit Kumar joins a16z general partner Anjney Midha for a deep dive into the research and engineering behind their voice technology. They discuss the technical challenges of real-time speech generation, the trade-offs in balancing personality with efficiency, and why the team is open-sourcing key components of their model. Ankit breaks down the complexities of multimodal AI, full-duplex conversation modeling, and the computational optimizations that enable low-latency interactions. They also explore the evolution of natural language as a user interface and its potential to redefine human-computer interaction.Plus, we take audience questions on everything from scaling laws in speech synthesis to the role of in-context learning in making AI voices more expressive.Key Takeaways:How Sesame AI achieves natural voice interactions through real-time speech generation.The impact of open-sourcing their speech model and what it means for AI research.The role of full-duplex modeling in improving AI responsiveness.How computational efficiency and system latency shape AI conversation quality.The growing role of natural language as a user interface in AI-driven experiences.For anyone interested in AI and voice technology, this episode offers an in-depth look at the latest advancements pushing the boundaries of human-computer interaction.Learn more:The Maya + Miles demoCrossing the uncanny valley of conversational voiceSesame CSM 1B modelFollow everybody on X:Ankit KumarAnjney Midha Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

DPS Nacharam

Dr. Emily Alsentzer joins hosts Raj Manrai and Andy Beam on NEJM AI Grand Rounds to discuss the evolution of natural language processing (NLP) in medicine. A Stanford faculty member and expert in clinical AI, Emily shares her journey from pre-med to biomedical AI, the role of language models in medical decision-making, and the ethical considerations surrounding bias in AI. The conversation explores everything from the early days of rule-based NLP to the modern era of large language models, the challenges of evaluating AI in clinical settings, and what the future holds for open-source medical AI. Transcript.

கணக்கியல் நுண்ணறிவு AI – ACCOUNTING INTELLIGENCE & FI : Financial IntelligenceFI: Financial Intelligenceகணக்கியல் நுண்ணறிவுகணநேர மதிப்பு மிகு இயல்புஇணங்கிய நிலைய கால அளவுகணமும் இயங்கும் சுற்றும் சுழல்நாணய மதிப்பீடு தொகுதி தரவு.தரவுக் கூறும் வகை நிலைவரவு செலவு கணக்கு வைப்பு பதிவுதரவுத்தள பகுப்பு அளவறியும் கூறுபாடுபரவும் நிலைப்பு ஆற்றல் இலக்கு.இலக்கு குறியீடு கணக்கிருப்பு பேரளவுஇலக்கத்தின் கூற்று விளம்பும் படியாக்கம்இலக்கில் வைக்கும் அறிக்கைத் தளம்நிலவும் வல்லமை மேலாண்மை திறமை.திறமை துணிவு மேன்மை அளவிடும்திறந்தவெளி பசுமை இல்லச் சூழல்காற்றுசிறப்புறும் உருப்படிவ அமைப்பு நிலைத்தருக்கும்திறமுறை ஈட்டும் பொருள் வருவாய்.Abstracts:FI Environmental monitoring systems can analyze large datasets from sensors to monitor environmental parameters like air quality, water quality, and biodiversity, helping identify potential environmental issues. The Accounting Reality reference is to include the information contained in it using further more in usable manners.The sustainable development in utilizing artificial intelligence (AI) is within the accounting practices to promote environmentally and socially responsible for business operations.It is primarily by improving the accuracy, efficiency and comprehensiveness of environmental impact assessment and reporting.This article explores the AI driven models with accounting process enumeration with Machine Learning and Natural Language Processing with the abundant resource's available along with Ecolonomy measurement Values.Keywords: Monitor, Reality, Ecolonomy, Environmental Impact.——–Introduction:The environmental impact increases by improving data analysis based on various sources such as energy consumption, waste management system, etc., and should be evaluated in reality in accordance to the periodic destruction of the Earth on the environmental impact.The ‘goals', in accordance with Acrostic way of expression in English language may be defined as for Accounting Artificialintelligence is ‘G'enerally ‘O'btaining ‘A'ccounting ‘L'evel ‘S'ystem.Automated reportingAccounting intelligence, AI, is in the state of being machine learning in large-scale language modeling to automate the process of collecting and compiling standard data, reducing manual errors and improving the efficiency of reporting tables.The Predictive analytics would provide by analyzing historical data, Accounting Intelligence in AI can predict potential environmental risks and opportunities, allowing companies to proactively implement sustainable development goal strategies.Predictive Analytics By analyzing historical data, accounting intelligence in AI can predict potential environmental risks and opportunities, enabling organizations to proactively implement sustainable growth targeting strategies.Supply chain transparency in managing account details.AI can be used to a large extent to analyze supplier data and identify potential sustainability risks in the supply chain.AI with supply chain management accounting intelligence promotes responsible sources of practicing at every stage of production at diversity levels.The Stakeholder engagement with AI-powered dashboards can provide clear and accessible sustainability information to stakeholders, enhancing transparency and accountability.Several examples of AI applications in sustainable accounting inCarbon footprint calculation method.Ecolonomy (collectively termed eco-economy) combined with AI impact on carbon footprint accounting can help in the process of quantifying the greenhouse gas (GHG) emissions of an individual's or multiple companies' products.A management accountant of any company with artificial intelligence can input data, analyze large datasets to identify patterns and trends, and predict future financial values.They can also detect anomalies that indicates fraudster insights to support better decisions making in the hierarchy application.‘Values' defines as ‘ Visionary Accounting Leverage Unique Environmental Situation', in Acrostic Definition for Ecolonomy.In The Digitalization era, Ecolonomy as per the view of Bert Kroese, The Term GDP is a poor measure of welfare. The GDP Values focuses on the present value and ignores the future. The depletion sources of Production capacities does not portrait the value future prosperity.The Tectonic Shifts in the Global Ecolonomy need to shift its information on Treating Data as Produced asset and itsFintech Related environmental impact assessment and allocation of resources can provide adequate protection consumption values, compiling Ecolonomy activity with the system of national accounts.Environmental Assessment:The AI concept for strengthening of environmental accounting and sustainable finance on measuring and transforming investment decisions has been in the processing taken into consideration with accurate for an organisation striving for effective climate action.Accounting for globalization understanding with Special Purpose Enterprises (SPE) can be calculated by sensors in factories, satellites producing images of forests.Based on the company's development designs, the company's emissions process profile with Multinational companies can be compared and analyzed within a national accounting context.A sustainable ecosystem of accounting for well-being at diversity levels is within the process of the National Accounting System.Food Growth along with distribution of House Hold Amenities can be measured and using the Present AI driven models.In the process of environmental degradation, the production cost frontier level does not depict the correct information of pollution in the process of the air atmosphere.Natural capital as a distinct category of any enterprise has a large direction of atmospheric pressure and pollution levels in the destruction of earthly living standards.The common shareable resources of degraded soil atmospheres are to promote the common shareable attributes of national accountability. Thus, a global competence center can focus on the development and use of natural resource assessment with Environmental Accounting Reality.The Common public shareable resources of ‘Ecolonomy' can be measured by calculating plus and minus Values in Genuine Progress Indicator (GPI) in Graded Existence.These are the best way in which to measure the climate impact of an entity's activities.The Consumption data can accurately value and calculate a company's carbon footprint by analyzing energy level with AI model. AI can analyze waste management system on the disposal patterns to identify areas for improvement and reduce waste generation.The Water level usage monitoring apparatus withAI-enabled sensors can monitor water consumption in real-time, allowing for efficient water management.The accuracy of AI-driven with abundant challenges and considerations can provide with the sustainability reporting depends heavily on the quality of data collected.AI's algorithms should be designed to avoid bias and promote responsible decision-making with good ethical standards.The Accuracy of an organizations environmental, social governance especially for Sustainability report should involve on the current trends with historical data reporting.The Implementation costs for AI systems to report on ever increasing temperature is required to picturize in at every moment in the Industrial sectors so as to alert the significant improvements of Climate Changes and along with the Common shareable cost of Shareable resources futuristic upfront cost of investment decisions in general. For AI model, attention has to carry forward to make the deployment of AI Ethical standards with responsibility accounting.‘AI holds significant promise for transforming sustainability accounting by improving environmental impact assessment and reporting.'Through advanced data analytics, real-time monitoring and improved transparency, AI can help organizations achieve their sustainability goals, ensuring a more sustainable future.Predictive modeling:AI can build complex models to predict future environmental changes based on current data, enabling proactive measures to mitigate negative impacts.Optimization algorithms:AI can optimize resource allocation and energy usage in various systems, like smart grids and industrial processes, leading to reduced environmental footprint.A life cycle assessment method can be used to assess the overall environmental impact of an artificial intelligence system, considering its development, operation and disposal phases.Artificial Intelligence can contribute to ecological values in different types.AI-powered image of Wildlife conservation recognition systems can monitor animal populations and identify threats to endangered species.AI can analyze large climate datasets to predict future weather patterns and potential climate change impacts with Climate change modelling.Agricultural operations should have sufficient impact on artificial intelligence levels.It improves irrigation and fertilizer use in agriculture, reducing water wastage and environmental impact.AI can improve waste sorting and recycling efficiency by identifying different waste materials.Important considerations when measuring ecological values:Data quality with quantification should haveAccurate and reliable environmental data which is crucial for effective AI analysis.Ethical implicationsAI systems must be developed and used responsibly to avoid unintended negative environmental consequences.The Transparency involves in Account figures conclusions and explainability with the facts is to be understandable with the relevant AI model reaches its conclusions is important for evaluating its environmental impact.Conclusion:The Common public development in the surroundings is a permanent feature for encouraging people to understand and using in unity in diversity with Accounting in Reality.There are a few key points for quantifying environmental values in AIAn environmental monitoring system problem should be readily accounted for. Transparency includes the results of accounting statistics and can be interpreted with facts, which is important to assess its environment when reaching its conclusions with the relevant AI model.Reference: Information about Ecolonomy and Econology in ACROSTICWay in the below you tube video link.(https://youtu.be/CbLLKevHaVI?si=jyALNvIDUgGe7xrz

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

In this episode we are looking at an area which impacts every business in the world. Unstructured data - that is, how we can start to squeeze insight from the piles of text, audio, video, and every other type of data that doesn't fit into a neat table.Carefully analysed, it can contain valuable insight, to be compared against other more traditional metrics such as sales figures, or economic results.Joining us to discuss is Gokul Sathiacama, VP of data storage for AI at Hewlett Packard Enterprise.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it. About this week's guest, Gokul Sathiacama: https://www.linkedin.com/in/gokuls/Sources cited in this week's episode:Statistics on global data generation: https://www.statista.com/statistics/871513/worldwide-data-created/Statistics on global IOT devices: https://paxtechnica.org/?page_id=738#:~:text=%E2%80%9COur%20IoT%20world%20is%20growing,billion%20by%202020.%E2%80%9D%20Intel.&text=Gartner.&text=Cisco.,-2011&text=%E2%80%9CGlobal%20M2M%20connections%20will%20increase,at%20the%20end%20of%202022.Global Web Index stats on smart devices: https://www.globalwebindex.net/

The rise of artificial intelligence (AI) is transforming industries, and customer success is no exception. Current trends show a rapid increase in AI adoption. This is driven by the potential to personalise interactions, automate routine tasks, and gain valuable insights from customer data among other solutions. However, the transition requires careful consideration of how AI can blend with existing customer success practices. The goal is to ultimately develop a combination of AI capabilities and human empathy, leading to more satisfying and effective customer experiences.This is where natural language processing (NLP) comes in. The power of NLP can be leveraged to understand customer queries and sentiment analysis to determine their emotional state. In this episode, Kevin Petrie, VP of Research at BARC, speaks to Kate Neal, Senior Director of Customer Success at Gainsight, about the evolving role of AI in customer success. TakeawaysAI adoption in customer success is accelerating despite some hesitations.Gainsight provides a comprehensive customer operating system, “powered by AI”.Natural language processing can significantly enhance customer sentiment analysis.Human oversight is crucial in AI applications to ensure accuracy.Data quality is essential for effective AI implementation.AI can help reduce the administrative burden on customer success teams.Collaboration between data and AI teams is necessary for success.Understanding AI's capabilities is key for customer success leaders.AI is not a replacement for human jobs but a tool to enhance them.Chapters00:00 Introduction to AI in Customer Success03:44 Gainsight's Role in Customer Success07:11 AI Adoption Trends in Customer Service10:51 Use Cases of AI in Customer Success15:12 Natural Language Processing and Customer Sentiment19:48 Human Oversight in AI Applications22:06 Collaboration Between Data and AI Teams23:59 Getting Started with AI in Customer Service

Bio   Bala has rich experience in retail technology and process transformation. Most recently, he worked as a Principal Architect for Intelligent Automation, Innovation & Supply Chain in a global Fortune 100 retail corporation. Currently he works for a luxury brand as Principal Architect for Intelligent Automation providing technology advice for the responsible use of technology (Low Code, RPA, Chatbots, and AI). He is passionate about technology and spends his free time reading, writing technical blogs and co-chairing a special interest group with The OR Society.   Interview Highlights 02:00 Mentors and peers 04:00 Community bus 07:10 Defining AI 08:20 Contextual awareness 11:45 GenAI 14:30 The human loop 17:30 Natural Language Processing 20:45 Sentiment analysis 24:00 Implementing AI solutions 26:30 Ethics and AI 27:30 Biased algorithms 32:00 EU AI Act  33:00 Responsible use of technology   Connect   Bala Madhusoodhanan on LinkedIn     Books and references   ·       https://nymag.com/intelligencer/article/ai-artificial-intelligence-chatbots-emily-m-bender.html  - NLP   ·       https://www.theregister.com/2021/05/27/clearview_europe/  - Facial Technology Issue   ·       https://www.designnews.com/electronics-test/apple-card-most-high-profile-case-ai-bias-yet  - Apple Card story   ·       https://www.ft.com/content/2d6fc319-2165-42fb-8de1-0edf1d765be3  - Data Centre growth   ·       https://www.technologyreview.com/2024/02/06/1087793/what-babies-can-teach-ai/   ·       Independent Audit of AI Systems -   ·       Home | The Alan Turing Institute   ·       Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World, Marco Iansiti & Karim R. Lakhani   ·       AI Superpowers: China, Silicon Valley, and the New World, Kai-Fu Lee   ·       The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You, Mike Walsh   ·       Human+Machine: Reimagining Work in the Age of AI, Paul R Daugherty, H. James Wilson   ·       Superintelligence: Paths, Dangers, Strategies, Nick Bostrom   ·       The Alignment Problem: How Can Artificial Intelligence Learn Human Values, Brian Christian   ·       Ethical Machines: Your Concise Guide to Totally Unbiased, Transparent, and Respectful AI, Reid Blackman   ·       Wanted: Human-AI Translators: Artificial Intelligence Demystified, Geertrui Mieke De Ketelaere   ·       The Future of Humanity: Terraforming Mars, Interstellar Travel, Immortality, and Our Destiny Beyond, Michio Kaku, Feodor Chin et al  Episode Transcript Intro: Hello and welcome to the Agile Innovation Leaders podcast. I'm Ula Ojiaku. On this podcast I speak with world-class leaders and doers about themselves and a variety of topics spanning Agile, Lean Innovation, Business, Leadership and much more – with actionable takeaways for you the listener. Ula Ojiaku So I have with me here, Bala Madhusoodhanan, who is a principal architect with a global luxury brand, and he looks after their RPA and AI transformation. So it's a pleasure to have you on the Agile Innovation Leaders podcast, Bala, thank you for making the time. Bala Madhusoodhanan It's a pleasure to have a conversation with the podcast and the podcast audience, Ula. I follow the podcast and there have been fantastic speakers in the past. So I feel privileged to join you on this conversation. Ula Ojiaku Well, the privilege is mine. So could you start off with telling us about yourself Bala, what have been the key points or the highlights of your life that have led to you being the Bala we know now? Bala Madhusoodhanan It's putting self into uncharted territory. So my background is mechanical engineering, and when I got the job, it was either you go into the mechanical engineering manufacturing side or the software side, which was slightly booming at that point of time, and obviously it was paying more then decided to take the software route, but eventually somewhere the path kind of overlapped. So from a mainframe background, started working on supply chain, and then came back to optimisation, tied back to manufacturing industry. Somewhere there is an overlap, but yeah, that was the first decision that probably got me here. The second decision was to work in a UK geography, rather than a US geography, which is again very strange in a lot of my peers. They generally go to Silicon Valley or East Coast, but I just took a choice to stay here for personal reasons. And then the third was like the mindset. I mean, I had over the last 15, 20 years, I had really good mentors, really good peers, so I always had their help to soundboard my crazy ideas, and I always try to keep a relationship ongoing. Ula Ojiaku What I'm hearing is, based on what you said, lots of relationships have been key to getting you to where you are today, both from mentors, peers. Could you expand on that? In what way? Bala Madhusoodhanan The technology is changing quite a lot, at least in the last 10 years. So if you look into pre-2010, there was no machine learning or it was statistics. People were just saying everything is statistics and accessibility to information was not that much, but post 2010, 2011, people started getting accessibility. Then there was a data buzz, big data came in, so there were a lot of opportunities where I could have taken a different career path, but every time I was in a dilemma which route to take, I had someone with whom either I have worked or who was my team lead or manager to guide me to tell me, like, take emotion out of the decision making and think in a calm mind, because you might jump into something and you might like it, you might not like it, you should not regret it. So again, over the course of so many such decisions, my cognitive mind has also started thinking about it. So those conversations really help. And again, collective experience. If you look into the decision making, it's not just my decision, I'm going through conversations that I had with people where they have applied their experience, so it's not just me or just not one situation, and to understand the why behind that, and that actually helps. In short, it's like a collection of conversations that I had with peers. A few of them are visionary leaders, they are good readers. So they always had a good insight on where I should focus, where I shouldn't focus, and of late recently, there has been a community bus. So a lot of things are moving to open source, there is a lot of community exchange of conversation, the blogging has picked up a lot. So, connecting to those parts also gives you a different dimension to think about. Ula Ojiaku So you said community bus, some of the listeners or people who are watching the video might not understand what you mean by the community bus. Are you talking about like meetups or communities that come around to discuss shared interests? Bala Madhusoodhanan If you are very much specifically interested in AI, or you are specifically interested in, power platform or a low code platform, there are a lot of content creators on those topics. You can go to YouTube, LinkedIn, and you get a lot of information about what's happening. They do a lot of hackathons, again, you need to invest time in all these things. If you don't, then you are basically missing the boat, but there are various channels like hackathon or meetup groups, or, I mean, it could be us like a virtual conversation like you and me, we both have some passionate topics, that's why we resonate and we are talking about it. So it's all about you taking an initiative, you finding time for it, and then you have tons and tons of information available through community or through conferences or through meetup groups. Ula Ojiaku Thanks for clarifying. So, you said as well, you had a collection of conversations that helped you whenever you were at a crossroad, some new technology or something emerges or there's a decision you had to make and checking in with your mentors, your peers and your personal Board of Directors almost, that they give you guidance. Now, looking back, would you say there were some turns you took that knowing what you know now, you would have done differently? Bala Madhusoodhanan I would have liked to study more. That is the only thing, because sometimes the educational degree, even though without a practical knowledge has a bigger advantage in certain conversation, otherwise your experience and your content should speak for you and it takes a little bit of effort and time to get that trust among leaders or peers just to, even them to trust saying like, okay, this person knows what he's talking about. I should probably trust rather than, someone has done a PhD and it's just finding the right balance of when I should have invested time in continuing my education, if I had time, I would have gone back two years and did everything that I had done, like minus two years off-set it by two years earlier. It would have given me different pathways. That is what I would think, but again, it's all constraints. I did the best at that point in time with whatever constraints I had. So I don't have any regret per se, but yeah, if there is a magic wand, I would do that. Ula Ojiaku So you are a LinkedIn top voice from AI. How would you define AI, artificial intelligence? Bala Madhusoodhanan I am a bit reluctant to give a term Artificial Intelligence. It's in my mind, it is Artificial Narrow Intelligence, it's slightly different. So let me start with a building block, which is machine learning. So machine learning is like a data labeller. You go to a Tesco store, you read the label, you know it is a can of soup because you have read the label, your brain is not only processing that image, it understands the surrounding. It does a lot of things when you pick that can of soup. You can't expect that by just feeding one model to a robot. So that's why I'm saying like it's AI is a bit over glorified in my mind. It is artificial narrow intelligence. What you do to automate certain specific tasks using a data set which is legal, ethical, and drives business value is what I would call machine learning, but yeah, it's just overhyped and heavily utilised term AI. Ula Ojiaku You said, there's a hype around artificial intelligence. So what do you mean by that? And where do you see it going? Bala Madhusoodhanan Going back to the machine learning definition that I said, it's basically predicting an output based on some input. That's as simple as what we would say machine learning. The word algorithm is basically something like a pattern finder. What you're doing is you are giving a lot of data, which is properly labelled, which has proper diversity of information, and there are multiple algorithms that can find patterns. The cleverness or engineering mind that you bring in is to select which pattern or which algorithm you would like to do for your use case. Now you're channelling the whole machine learning into one use case. That's why I'm going with the term narrow intelligence. Computers can do brilliant jobs. So you ask computers to do like a Rubik's cubes solving. It will do it very quickly because the task is very simple and it is just doing a lot of calculation. You give a Rubik's cube to a kid. It has to apply it. The brain is not trained enough, so it has to cognitively learn. Maybe it will be faster. So anything which is just pure calculation, pure computing, if the data is labelled properly, you want to predict an outcome, yes, you can use computers. One of the interesting videos that I showed in one of my previous talks was a robot trying to walk across the street. This is in 2018 or 19. The first video was basically talking about a robot crossing a street and there were vehicles coming across and the robot just had a headbutt and it just fell off. Now a four year old kid was asked to walk and it knew that I have to press a red signal. So it went to the signal stop. It knew, or the baby knew that I can only walk when it is green. And then it looks around and then walks so you can see the difference – a four year old kid has a contextual awareness of what is happening, whereas the robot, which is supposed to be called as artificial intelligence couldn't see that. So again, if you look, our human brains have been evolved over millions of years. There are like 10 billion neurons or something, and it is highly optimised. So when I sleep, there are different set of neurons which are running. When I speak to you, my eyes and ears are running, my motion sensor neurons are running, but these are all highly optimised. So the mother control knows how much energy should be sent on which neuron, right, whereas all these large language models, there is only one task. You ask it, it's just going to do that. It doesn't have that intelligence to optimise. When I sleep, maybe 90 percent of my neurons are sleeping. It's getting recharged. Only the dream neurons are working. Whereas once you put a model live, it doesn't matter, all the hundred thousand neurons would run. So, yeah, it's in very infancy state, maybe with quantum computing, maybe with more power and better chips things might change, but I don't see that happening in the next five to 10 years. Ula Ojiaku Now, what do you say about Gen AI? Would you also classify generative AI as purely artificial neural intelligence? Bala Madhusoodhanan The thing with generative AI is you're trying to generalise a lot of use cases, say ChatGPT, you can throw in a PDF, you can ask something, or you can say, hey, can you create a content for my blog or things like that, right? Again, all it is trying to do is it has some historical content with which it is trying to come up with a response. So the thing that I would say is humans are really good with creativity. If a problem is thrown at a person, he will find creative ways to solve it. The tool with which we are going to solve might be a GenAI tool, I don't know, because I don't know the problem, but because GenAI is in a hype cycle, every problem doesn't need GenAI, that's my view. So there was an interesting research which was done by someone in Montreal University. It talks about 10 of the basic tasks like converting text to text or text to speech and with a generative AI model or multiple models, because you have a lot of vendors providing different GenAI models, and then they went with task specific models and the thing that they found was the task specific models were cheap to run, very, very scalable and robust and highly accurate, right. Whereas GenAI, if, when you try to use it and when it goes into a production ready or enterprise ready and if it is used by customers or third party, which are not part of your ecosystem, you are putting yourself in some kind of risk category. There could be a risk of copyright issues. There could be a risk of IP issues. There could be risk of not getting the right consent from someone. I can say, can you create an image of a podcaster named Ula? You never know because you don't remember that one of your photos on Google or Twitter or somewhere is not set as private. No one has come and asked you saying, I'm using this image. And yeah, it's finding the right balance. So even before taking the technology, I think people should think about what problem are they trying to solve? In my mind, AI or artificial intelligence, or narrow intelligence can have two buckets, right. The first bucket is to do with how can I optimise the existing process? Like there are a lot of things that I'm doing, is there a better way to do it? Is there an efficient way to do it? Can I save time? Can I save money? Stuff like that. So that is an optimisation or driving efficiency lever. Other one could be, I know what to do. I have a lot of data, but I don't have infrastructure or people to do it, like workforce augmentation. Say, I have 10 data entry persons who are graduate level. Their only job is to review the receipts or invoices. I work in FCA. I have to manually look at it, approve it, and file it, right? Now it is a very tedious job. So all you are doing is you are augmenting the whole process with an OCR engine. So OCR is Optical Character Recognition. So there are models, which again, it's a beautiful term for what our eyes do. When we travel somewhere, we get an invoice, we exactly know where to look, right? What is the total amount? What is the currency I have paid? Have they taken the correct credit card? Is my address right? All those things, unconsciously, your brain does it. Whereas our models given by different software vendors, which have trained to capture these specific entities which are universal language, to just pass, on data set, you just pass the image on it. It just picks and maps that information. Someone else will do that job. But as part of your process design, what you would do is I will do the heavy lifting of identifying the points. And I'll give it to someone because I want someone to validate it. It's human at the end. Someone is approving it. So they basically put a human in loop and, human centric design to a problem solving situation. That's your efficiency lever, right? Then you have something called innovation level - I need to do something radical, I have not done this product or service. Yeah, that's a space where you can use AI, again, to do small proof of concepts. One example could be, I'm opening a new store, it's in a new country, I don't know how the store layout should look like. These are my products. This is the store square footage. Can you recommend me the best way so that I can sell through a lot? Now, a visual merchandising team will have some ideas on where the things should be, they might give that prompt. Those texts can be converted into image. Once you get the base image, then it's human. It's us. So it will be a starting point rather than someone implementing everything. It could be a starting point. But can you trust it? I don't know. Ula Ojiaku And that's why you said the importance of having a human in the loop. Bala Madhusoodhanan Yeah. So the human loop again, it's because we humans bring contextual awareness to the situation, which machine doesn't know. So I'll tie back this to the NLP. So Natural Language Processing, it has two components, so you have natural language understanding and then you have natural language generation. When you create a machine learning model, all it is doing is, it is understanding the structure of language. It's called form. I'm giving you 10,000 PDFs, or you're reading a Harry Potter book. There is a difference between you reading a Harry Potter book and the machine interpreting that Harry Potter book. You would have imagination. You will have context of, oh, in the last chapter, we were in the hilly region or in a valley, I think it will be like this, the words like mist, cold, wood. You started already forming images and visualising stuff. The machine doesn't do that. Machine works on this is the word, this is a pronoun, this is the noun, this is the structure of language, so the next one should be this, right? So, coming back to the natural language understanding, that is where the context and the form comes into play. Just think of some alphabets put in front of you. You have no idea, but these are the alphabet. You recognise A, you recognise B, you recognise the word, but you don't understand the context. One example is I'm swimming against the current. Now, current here is the motion of water, right? My current code base is version 01. I'm using the same current, right? The context is different. So interpreting the structure of language is one thing. So, in natural language understanding, what we try to do is we try to understand the context. NLG, Natural Language Generation, is basically how can I respond in a way where I'm giving you an answer to your query. And this combined is NLP. It's a big field, there was a research done, the professor is Emily Bender, and she one of the leading professors in the NLP space. So the experiment was very funny. It was about a parrot in an island talking to someone, and there was a shark in between, or some sea creature, which basically broke the connection and was listening to what this person was saying and mimicking. Again, this is the problem with NLP, right? You don't have understanding of the context. You don't put empathy to it. You don't understand the voice modulation. Like when I'm talking to you, you can judge what my emotion cues are, you can put empathy, you can tailor the conversation. If I'm feeling sad, you can put a different spin, whereas if I'm chatting to a robot, it's just going to give a standard response. So again, you have to be very careful in which situation you're going to use it, whether it is for a small team, whether it is going to be in public, stuff like that. Ula Ojiaku So that's interesting because sometimes I join the Masters of Scale strategy sessions and at the last one there was someone whose organisational startup was featured and apparently what their startup is doing is to build AI solutions that are able to do sentiment analysis. And I think some of these, again, in their early stages, but some of these things are already available to try to understand the tone of voice, the words they say, and match it with maybe the expression and actually can transcribe virtual meetings and say, okay, this person said this, they looked perplexed or they looked slightly happy. So what do you think about that? I understand you're saying that machines can't do that, but it seems like there are already organisations trying to push the envelope towards that direction. Bala Madhusoodhanan So the example that you gave, sentiment of the conversation, again, it is going by the structure or the words that I'm using. I am feeling good. So good, here is positive sentiment. Again, for me the capability is slightly overhyped, the reason being is it might do 20 percent or 30 percent of what a human might do, but the human is any day better than that particular use case, right? So the sentiment analysis typically works on the sentiment data set, which would say, these are the certain proverbs, these are the certain types of words, this generally referred to positive sentiment or a good sentiment or feel good factor, but the model is only good as good as the data is, right? So no one is going and constantly updating that dictionary. No one is thinking about it, like Gen Z have a different lingo, millennials had a different lingo. So, again, you have to treat it use case by use case, Ula. Ula Ojiaku At the end of the day, the way things currently are is that machines aren't at the place where they are as good as humans. Humans are still good at doing what humans do, and that's the key thing. Bala Madhusoodhanan Interesting use case that I recently read probably after COVID was immersive reading. So people with dyslexia. So again, AI is used for good as well, I'm not saying it is completely bad. So AI is used for good, like, teaching kids who are dyslexic, right? Speech to text can talk, or can translate a paragraph, the kid can hear it, and on the screen, I think one note has an immersive reader, it actually highlights which word it is, uttering into the ears and research study showed that kids who were part of the study group with this immersive reading audio textbook, they had a better grasp of the context and they performed well and they were able to manage dyslexia better. Now, again, we are using the technology, but again, kudos to the research team, they identified a real problem, they formulated how the problem could be solved, they were successful. So, again, technology is being used again. Cancer research, they invest heavily, in image clustering, brain tumours, I mean, there are a lot of use cases where it's used for good, but then again, when you're using it, you just need to think about biases. You need to understand the risk, I mean, everything is risk and reward. If your reward is out-paying the minimum risk that you're taking, then it's acceptable. Ula Ojiaku What would you advise leaders of organisations who are considering implementing AI solutions? What are the things we need to consider? Bala Madhusoodhanan Okay. So going back to the business strategy and growth. So that is something that the enterprises or big organisations would have in mind. Always have your AI goals aligned to what they want. So as I said, there are two buckets. One is your efficiency driver, operational efficiency bucket. The other one is your innovation bucket. Just have a sense check of where the business wants to invest in. Just because AI is there doesn't mean you have to use it right. Look into opportunities where you can drive more values. So that would be my first line of thought. The second would be more to do with educating leaders about AI literacy, like what each models are, what do they do? What are the pitfalls, the ethical awareness about use of AI, data privacy is big. So again, that education is just like high level, with some examples on the same business domain where it has been successful, where it has been not so successful, what are the challenges that they face? That's something that I would urge everyone to invest time in. I think I did mention about security again, over the years, the practice has been security is always kept as last. So again, I was fortunate enough to work in organisations where security first mindset was put in place, because once you have a proof of value, once you show that to people, people get excited, and it's about messaging it and making sure it is very secured, protecting the end users. So the third one would be talking about having secure first design policies or principles. Machine learning or AI is of no good if your data quality is not there. So have a data strategy is something that I would definitely recommend. Start small. I mean, just like agile, you take a value, you start small, you realise whether your hypothesis was correct or not, you monitor how you performed and then you think about scale just by hello world doesn't mean that you have mastered that. So have that mindset, start small, monitor, have constant feedback, and then you think about scaling. Ula Ojiaku What are the key things about ethics and AI, do you think leaders should be aware of at this point in time? Bala Madhusoodhanan So again, ethical is very subjective. So it's about having different stakeholders to give their honest opinion of whether your solution is the right thing to do against the value of the enterprise. And it's not your view or my view, it's a consent view and certain things where people are involved, you might need to get HR, you might need to get legal, you might need to get brand reputation team to come and assist you because you don't understand the why behind certain policies were put in place. So one is, is the solution or is the AI ethical to the core value of the enterprise? So that's the first sense check that you need to do. If you pass that sense check, then comes about a lot of other threats, I would say like, is the model that I'm using, did it have a fair representation of all data set? There's a classic case study on one of a big cloud computing giant using an AI algorithm to filter resumes and they had to stop it immediately because the data set was all Ivy League, male, white, dominant, it didn't have the right representation. Over the 10 years, if I'm just hiring certain type of people, my data is inherently biased, no matter how good my algorithm is, if I don't have that data set. The other example is clarify AI. They got into trouble on using very biased data to give an outcome on some decision making to immigration, which has a bigger ramification. Then you talk about fairness, whether the AI system is fair to give you an output. So there was a funny story about a man and a woman in California living together, and I think the woman wasn't provided a credit card, even though everything, the postcode is the same, both of them work in the same company, and it was, I think it has to do with Apple Pay. Apple Pay wanted to bring in a silver credit card, Apple card or whatever it is, but then it is so unfair that the women who was equally qualified was not given the right credit limit, and the bank clearly said the algorithm said so. Then you have privacy concern, right? So all these generic models that you have that is available, even ChatGPT for that matter. Now you can chat with ChatGPT multiple times. You can talk about someone like Trevor Noah and you can say hey, can you create a joke? Now it has been trained with the jokes that he has done, it might be available publicly. But has the creator of model got a consent saying, hey Trevor, I'm going to use your content so that I can give better, and how many such consent, even Wikipedia, if you look into Wikipedia, about 80 percent of the information is public, but it is not diversified. What I mean by that is you can search for a lot of information. If the person is from America or from UK or from Europe, maybe from India to some extent, but what is the quality of data, if you think about countries in Africa, what do you think about South America? I mean, it is not representing the total diversity of data, and we have this large language model, which has been just trained on that data, right? So there is a bias and because of that bias, your outcome might not be fair. So these two are the main things, and of course the privacy concern. So if someone goes and says, hey, you have used my data, you didn't even ask me, then you're into lawsuit. Without getting a proper consent, again, it's a bad world, it's very fast moving and people don't even, including me, I don't even read every terms and condition, I just scroll down, tick, confirm, but those things are the things where I think education should come into play. Think about it, because people don't understand what could go wrong, not to them, but someone like them. Then there is a big fear of job displacement, like if I put this AI system, what will I do with my workforce? Say I had ten people, you need to think about, you need to reimagine your workplace. These are the ten jobs my ten people are doing. If I augment six of those jobs, how can I use my ten resources effectively to do something different or that piece of puzzle is always, again, it goes back to the core values of the company, what they think about their people, how everything is back, but it's just that needs a lot of inputs from multiple stakeholders. Ula Ojiaku It ties back to the enterprise strategy, there is the values, but with technology as it has evolved over the years, things will be made obsolete, but there are new opportunities that are created, so moving from when people travelled with horses and buggies and then the automotive came up. Yes, there wasn't as much demand for horseshoes and horses and buggies, but there was a new industry, the people who would mechanics or garages and things like that. So I think it's really about that. Like, going back to what you're saying, how can you redeploy people? And that might involve, again, training, reskilling, and investing in education of the workforce so that they're able to harness AI and to do those creative things that you've emphasised over this conversation about human beings, that creative aspect, that ability to understand context and nuance and apply it to the situation. Bala Madhusoodhanan So I was fortunate to work with ForHumanity, an NGO which basically is trying to certify people to look into auditing AI systems. So EU AI Act is now in place, it will be enforced soon. So you need people to have controls on all these AI systems to protect - it's done to protect people, it's done to protect the enterprise. So I was fortunate enough to be part of that community. I'm still working closely with the Operation Research Society. Again, you should be passionate enough, you should find time to do it, and if you do it, then the universe will find a way to give you something interesting to work with. And our society, The Alan Turing Institute, the ForHumanity Society, I had a few ICO workshops, which was quite interesting because when you hear perspectives from people from different facets of life, like lawyers and solicitors, you would think, ah, this statement, I wouldn't interpret in this way. It was a good learning experience and I'm sure if I have time, I would still continue to do that and invest time in ethical AI. As technology, it's not only AI, it's ethical use of technology, so sustainability is also part of ethical bucket if you look into it. So there was an interesting paper it talks about how many data centres have been opened between 2018 to 2024, which is like six years and the power consumption has gone from X to three times X or two times X, so we have opened a lot. We have already caused damage to the environment with all these technology, and just because the technology is there, it doesn't mean you have to use it, but again, it's that educational bit, what is the right thing to do? And even the ESG awareness, people are not aware. Like now, if you go to the current TikTok trenders, they know I need to look into certified B Corp when I am buying something. The reason is because they know, and they're more passionate about saving the world. Maybe we are not, I don't know, but again, once you start educating and, telling those stories, humans are really good, so you will have a change of heart. Ula Ojiaku What I'm hearing you say is that education is key to help us to make informed choices. There is a time and place where you would need to use AI, but not everything requires it, and if we're more thoughtful in how we approach, these, because these are tools at the end of the day, then we can at least try to be more balanced in the risks and taking advantage of opportunities versus the risks around it and the impact these decisions and the tools that we choose to use make on the environment. Now, what books have you found yourself recommending most to people, and why? Bala Madhusoodhanan Because we have been talking on AI, AI Superpower is one book which was written by Kai-Fu Lee. There is this book by Brian Christian, The Alignment Problem: Machine Learning and Human Values alignment of human values and machine it was basically talking about what are the human values? Where do you want to use machine learning? How do you basically come up with a decision making, that's a really interesting read. Then there is a book called Ethical Machines by Reid Blackman. So it talks about all the ethical facets of AI, like biases, fairnesses, like data privacy, transparency, explainability, and he gives quite a detail, example and walkthrough of what that means. Another interesting book was Wanted: Human-AI Translators: Artificial Intelligence Demystified by a Dutch professor, again, really, really lovely narration of what algorithms are, what AI is, where, and all you should think about, what controls and stuff like that. So that is an interesting book. Harvard Professor Kahrim Lakhani, he wrote something called, Competing in the Age of AI, that's a good book. The Algorithmic Leader: How to Be Smart When Machines Are Smarter Than You by Mike Walsh is another good book, which I finished a couple of months back. Ula Ojiaku And if the audience wants to find you, how can they reach out to you? Bala Madhusoodhanan They can always reach out to me at LinkedIn, I would be happy to touch base through LinkedIn. Ula Ojiaku Awesome. And do you have any final words and or ask of the audience? Bala Madhusoodhanan The final word is, again, responsible use of technology. Think about not just the use case, think about the environmental impact, think about the future generation, because I think the damage is already done. So, at least not in this lifetime, maybe three or four lifetimes down the line, it might not be the beautiful earth that we have. Ula Ojiaku It's been a pleasure, as always, speaking with you, Bala, and thank you so much for sharing your insights and wisdom, and thank you for being a guest on the Agile Innovation Leaders Podcast. Bala Madhusoodhanan Thank you, lovely conversation, and yeah, looking forward to connecting with more like minded LinkedIn colleagues. Ula Ojiaku That's all we have for now. Thanks for listening. If you liked this show, do subscribe at www.agileinnovationleaders.com or your favourite podcast provider. Also share with friends and do leave a review on iTunes. This would help others find this show. I'd also love to hear from you, so please drop me an email at ula@agileinnovationleaders.com Take care and God bless!   

Next in Creator Media spoke with Paul Greenberg, CEO of Butterworks, on how his company uses AI to help brands make more successful social video content, and why so far, the technology has been a net positive.  Still, Greenberg talked about the dangers of the proliferation of AI slop and why it's going to become challenging for consumers and brands to sort through what's real, what's not, and what kind of attention is most valuable.

Tobi Lütke is the founder and CEO of Shopify, a $130 billion business that powers over 10% of all U.S. e-commerce. Starting as a snowboard shop in 2004, Shopify has become the leading commerce platform by consistently approaching problems differently. Tobi remains deeply technical, frequently coding alongside his team, and is known for his unique approach to leadership, product development, and company building. In our conversation, we discuss:• Why complexity kills entrepreneurship• How to develop and leverage your unique talent stack• How specifically Tobi approaches thinking from first principles• The importance of focusing on unquantifiable qualities like joy and delight• Why Tobi works backward from a 100-year vision• Why metrics should support decisions, not make them• The power of following your curiosity• What Tobi believes it takes to be a great product leader• Much more—Brought to you by:• Sinch—Build messaging, email, and calling into your product• Liveblocks—Ready-made collaborative features to drop into your product• Loom—The easiest screen recorder you'll ever use—Find the transcript at: https://www.lennysnewsletter.com/p/tobi-lutkes-leadership-playbook—Where to find Tobi Lütke:• X: https://x.com/tobi• LinkedIn: https://www.linkedin.com/in/tobiaslutke/• Website: https://tobi.lutke.com/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Welcome and introduction(04:17) The Tobi tornado(07:10) Maximizing human potential(11:05) Education and personal growth(16:47) Operating without KPIs(25:00) First-principles thinking(40:04) Remote work(45:59) Why Tobi never stopped coding(54:46) Embracing disagreement(01:01:27) The 100-year vision(01:09:29) Balancing tactics and positioning(01:17:15) Encouraging entrepreneurship(01:19:34) The power of good UX(01:28:42) The talent stack and unique opportunities(01:34:30) The role of passion in product development(01:36:39) Final thoughts and farewell—Referenced:• How Shopify builds a high-intensity culture | Farhan Thawar (VP and Head of Eng): https://www.lennysnewsletter.com/p/how-shopify-builds-a-high-intensity-culture-farhan-thawar• Breaking the rules of growth: Why Shopify bans KPIs, optimizes for churn, prioritizes intuition, and builds toward a 100-year vision | Archie Abrams (VP Product, Head of Growth at Shopify): https://www.lennysnewsletter.com/p/shopifys-growth-archie-abrams• The ultimate guide to performance marketing | Timothy Davis (Shopify): https://www.lennysnewsletter.com/p/performance-marketing-timothy-davis• Brandon Chu on building product at Shopify, how writing changed the trajectory of his career, the habits that make you a great PM, pros and cons of being a platform PM, how Shopify got through Covid: https://www.lennysnewsletter.com/p/brandon-chu-on-what-its-like-to-build• IRC: https://en.wikipedia.org/wiki/IRC• Goodhart's law: https://en.wikipedia.org/wiki/Goodhart%27s_law• Glen Coates on LinkedIn: https://www.linkedin.com/in/glcoates/• How Shopify builds product: https://www.lennysnewsletter.com/p/how-shopify-builds-product• The Last Dance on Netflix: https://www.netflix.com/title/80203144• Autoregressive Models for Natural Language Processing: https://medium.com/@zaiinn440/autoregressive-models-for-natural-language-processing-b95e5f933e1f• Archimedean property: https://en.wikipedia.org/wiki/Archimedean_property• Tabula rasa: https://en.wikipedia.org/wiki/Tabula_rasa• Daniel Weinand on LinkedIn: https://www.linkedin.com/in/danielweinand/• World of Warcraft: https://worldofwarcraft.blizzard.com• Harley Finkelstein on LinkedIn: https://www.linkedin.com/in/harleyf/• Monorepo: https://en.wikipedia.org/wiki/Monorepo• The Sarbanes Oxley Act: https://sarbanes-oxley-act.com/• Shopify builds Shopify Balance with Stripe to give small businesses an easier way to manage money: https://stripe.com/customers/shopify• Stanford marshmallow experiment: https://en.wikipedia.org/wiki/Stanford_marshmallow_experiment• Brian Armstrong on LinkedIn: https://www.linkedin.com/in/barmstrong/• We are the Web: https://link.wired.com/public/32945405—Recommended books:• Finite and Infinite Games: https://www.amazon.com/Finite-Infinite-Games-James-Carse/dp/1476731713• The Infinite Game: https://www.amazon.com/Infinite-Game-Simon-Sinek/dp/073521350X/—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

In this episode of AI + a16z, a16z General Partner Martin Casado and Rasa cofounder and CEO Alan Nichol discuss the past, present, and future of AI agents and chatbots. Alan shares his history working to solve this problem with traditional natural language processing (NLP), expounds on how large language models (LLMs) are helping to dull the many sharp corners of natural-language interactions, and explains how pairing them with inflexible business logic is a great combination.Learn more:Task-Oriented Dialogue with In-Context LearningGoEX: Perspectives and Designs Towards a Runtime for Autonomous LLM ApplicationCALM SummitFollow everyone on X:Alan NicholMartin Casado Check out everything a16z is doing with artificial intelligence here, including articles, projects, and more podcasts.

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Já não faltavam evidências sobre os malefícios dos vapes e cigarros eletrônicos em 2019, quando falamos pela primeira vez sobre o assunto. Mas quais as novas evidências? E o seu consumo já se configura um problema de saúde pública?Este episódio é apresentado pela ACT Promoção da Saúde, organização não governamental que atua na promoção e defesa de políticas de saúde pública, especialmente nas áreas de controle do tabagismo, alimentação saudável, controle do álcool e atividade física. Esse trabalho é realizado por meio de ações de advocacy, que incluem incidência política, comunicação, mobilização, formação de redes e pesquisa, entre outras. Conheça mais em https://actbr.org.br/Confira o papo entre o leigo curioso, Ken Fujioka, e o cientista PhD, Altay de Souza.>> OUÇA (57min 41s)*Naruhodo! é o podcast pra quem tem fome de aprender. Ciência, senso comum, curiosidades, desafios e muito mais. Com o leigo curioso, Ken Fujioka, e o cientista PhD, Altay de Souza.Edição: Reginaldo Cursino.http://naruhodo.b9.com.br*APOIO: ACTEste episódio é apresentado pelo Sebrae Rio. Sabe aquelas pessoas que a gente admira pela criatividade, pela capacidade de liderar projetos ou de transformar ideias em realidade? Você pode ser uma dessas pessoas com o apoio do Sebrae Rio, desenvolvendo habilidades com a educação empreendedora, que não é só pra quem quer abrir um negócio: essas habilidades são superimportantes pra qualquer profissional.E se você é gestor ou professor de uma instituição de ensino, você pode levar a Educação Empreendedora para os seus alunos.É de graça e ainda emite certificado!Saiba mais, acessando atitude.sebraerj.com.br. E compartilhe suas habilidades empreendedoras nas redes sociais com a hashtag #TáNaSuaAtitude.Sebrae Rio: empreender tá na sua atitude.*REFERÊNCIASRethink Vape: Development and evaluation of a risk communication campaign to prevent youth E-cigarette usehttps://www.sciencedirect.com/science/article/pii/S0306460320307942?casa_token=stt22CU9-6AAAAAA:YPkZZ53Ftu3nkkekilolsWuJNKUbryiRjeLSIDReCt7I_VzpUe7m00pMu7x8ekXPen_tBRSmplYImpact of messages about scientific uncertainty on risk perceptions and intentions to use electronic vaping productshttps://www.sciencedirect.com/science/article/pii/S0306460318312140?casa_token=cLYGPqH_5ycAAAAA:ENqaVvNiFavJdpveZm6twD9JcfZP-EziEL0Vzt9gTE6wY4TLGguWJSDbG0-qZvIyTnMnkIyh3oIComics and Morals: Communicating the Risks of Vaping to Young Adults Through Moralized Graphic Comicshttps://www.proquest.com/openview/30046a092e0b52154768a5774baf4607/1?pq-origsite=gscholar&cbl=18750&diss=yHealth Messaging Strategies for Vaping Prevention and Cessation Among Youth and Young Adults: A Systematic Reviewhttps://www.tandfonline.com/doi/full/10.1080/10410236.2024.2352284Nicotina é até seis vezes maior em quem fuma cigarro eletrônico do que 20 cigarros comuns por diahttps://jornal.usp.br/ciencias/nicotina-e-ate-seis-vezes-maior-em-quem-fuma-cigarro-eletronico-do-que-20-cigarros-comuns-por-dia/Vaping in Youthhttps://jamanetwork.com/journals/jama/article-abstract/2822166?casa_token=AOMeZZluas0AAAAA:sLpdsaUTGQ6B9626AzCUq92sKEiOiQb4ZukceE2Z_lWxzYOfJ69UkK2sLlCNLiN9ulGOk1OzkJE&casa_token=j41vokSLcaUAAAAA:N7nCcnNEPuRTSdhY5abaMDWnmHMatAyw265mnYE3YUj1DOzb8Bt_VVuVMuPLwDh-amcoVdJ6_J8Trends in long term vaping among adults in England, 2013-23: population based studyhttps://www.bmj.com/content/386/bmj-2023-079016.shortA Systematic Review of Predictors of Vaping Cessation Among Young Peophttps://academic.oup.com/ntr/advance-article/doi/10.1093/ntr/ntae181/7717604A Vaping Cessation Text Message Program for Adolescent E-Cigarette Usershttps://jamanetwork.com/journals/jama/article-abstract/2822082?casa_token=jFrwYbTuE00AAAAA:cjSPTgP0FeIYTFS13Uli6akYcN37xjahDcnuCGSEXrgJQMpxExcD2GExrwPO4gNPdb2HqQ9Nyqc&casa_token=TrYwGgae_xEAAAAA:XCLLhI4Ku1KjcxxJ1tIi74OJmwW2Y1eNjq60LVYbJ7B8M2TNh7GPdQwBQIBjDefqVwlkmcaW7TUSmoking and vaping alter genes related to mechanisms of SARS-CoV-2 susceptibility and severity: a systematic review and meta-analysishttps://publications.ersnet.org/content/erj/64/1/2400133.abstractThe Impact of Vaping on the Ocular Surface: A Systematic Review of the Literaturehttps://www.mdpi.com/2077-0383/13/9/2619Drug Use Frequency Variation and Mental Health During the COVID-19 Pandemic: an Online Surveyhttps://pmc.ncbi.nlm.nih.gov/articles/PMC8404543/Vaping among adults in England who have never regularly smoked: a population-based study, 2016–24https://www.thelancet.com/journals/lanpub/article/PIIS2468-2667(24)00183-X/fulltextAssociation of vaping with respiratory symptoms in U.S. young adults: Nicotine, cannabis, and dual vapinghttps://www.sciencedirect.com/science/article/pii/S009174352400330XTobacco Harm Reduction: The Industry's Latest Trojan Horse?https://exposetobacco.org/wp-content/uploads/tobacco-harm-reduction-cop10.pdfU.S. retail sales data show 86% of e-cigarette sales are for illegal productshttps://truthinitiative.org/research-resources/tobacco-industry-marketing/us-retail-sales-data-show-86-e-cigarette-sales-are?trk=feed_main-feed-card_feed-article-contentThe Normalization of Vaping on TikTok Using Computer Vision, Natural Language Processing, and Qualitative Thematic Analysis: Mixed Methods Studyhttps://www.jmir.org/2024/1/e55591/From Smoking to Vaping: The Motivation for E-Cigarette Use at the Neurobiological Level – An fMRI Studyhttps://academic.oup.com/ntr/advance-article/doi/10.1093/ntr/ntae273/7906109Vaping and Smoking Cue Reactivity in Young Adult Nonsmoking Electronic Cigarette Users: A Functional Neuroimaging Studyhttps://academic.oup.com/ntr/advance-article-abstract/doi/10.1093/ntr/ntae257/7863347Impact of Electronic Cigarettes on the Cardiovascular Systemhttps://pmc.ncbi.nlm.nih.gov/articles/PMC5634286/Naruhodo #207 - Vape e cigarro eletrônico são seguros? (2019)https://www.youtube.com/watch?v=Raa9CUrIFbsNaruhodo #85 - Por que é tão difícil parar de fumar?https://www.youtube.com/watch?v=SPkIT0ehoisNaruhodo #49 - O que causa o vício?https://www.youtube.com/watch?v=--Z_ylPXIWcNaruhodo #94 - O que é o Teorema de Bayes? (E o que horóscopo tem a ver com isso?)https://www.youtube.com/watch?v=BE5fpsfPerwNaruhodo #328 - Existem "gatilhos mentais"?https://www.youtube.com/watch?v=fxBQJlin8Z4Naruhodo #419 - Maconha faz mal? - Parte 1 de 2https://www.youtube.com/watch?v=cvLTh2bKPiQNaruhodo #420 - Maconha faz mal? - Parte 2 de 2https://www.youtube.com/watch?v=F7wVcGvpoGANaruhodo #267 - O que é dissonância cognitiva?https://www.youtube.com/watch?v=1xJwqmir5UwNaruhodo #268 - O que é dissonância cognitiva? - Parte 2 de 2https://www.youtube.com/watch?v=--OHlHmOQTM*APOIE O NARUHODO PELA PLATAFORMA ORELO!O podcast Naruhodo está no Orelo: bit.ly/naruhodo-no-oreloE é por meio dessa plataforma de apoio aos criadores de conteúdo que você ajuda o Naruhodo a se manter no ar.Você escolhe um valor de contribuição mensal e tem acesso a conteúdos exclusivos, conteúdos antecipados e vantagens especiais.Além disso, você pode ter acesso ao nosso grupo fechado no Telegram, e conversar comigo, com o Altay e com outros apoiadores.E não é só isso: toda vez que você ouvir ou fizer download de um episódio pelo Orelo, vai também estar pingando uns trocadinhos para o nosso projeto.Então, baixe agora mesmo o app Orelo no endereço Orelo.CC ou na sua loja de aplicativos e ajude a fortalecer o conhecimento científico.bit.ly/naruhodo-no-orelo

Mustafa Suleyman on Copilot Vision, AI Companions, Infinite Memory, AI Agents, and moreCopilot Vision: A new era of human-computer interactionHow Microsoft AI is differentiating from OpenAIUser data privacy with Copilot VisionLiving amongst a co-intelligence in 10+ yearsMemory, learning, gaming, and AI agents​​AI ConsultationAI Consultation:Want to harness the power of AI for your business? Etienne Noumen, the creator of this podcast "AI Unraveled," is also a senior software engineer and AI consultant. He helps organizations across industries like yours (Oil and Gas, Medicine, Education, Amateur Sport, Finance, etc. ) leverage AI through custom training, integrations, mobile apps, or ongoing advisory services. Whether you're new to AI or need a specialized solution, Etienne can bridge the gap between technology and results. DM here or Email at info@djamgatech.com or Visit djamgatech.com to learn more and receive a personalized AI strategy for your business.AI Unraveled eBook:Master GPT-x, Gemini, Generative AI, LLMs, Prompt Engineering: A simplified Guide For Everyday Users: OpenAI, ChatGPT, Google Gemini, Anthropic Claude, Grok xAI, Generative AI, Large Language Models (LLMs), Llama, Deepmind, Explainable AI (XAI), Discriminative AI, AI Ethics, Machine Learning, Reinforcement Learning, Natural Language Processing, Neural networks, Intelligent agents, GPUs, Q*, RAG, Master Prompt Engineering, Pass AI CertificationsGet it at Apple at https://books.apple.com/us/book/id6445730691Get it at Google at: https://play.google.com/store/books/details?id=oySuEAAAQBAJAI and Machine Learning For Dummies ProDjamgatech has launched a new educational app on the Apple App Store, aimed at simplifying AI and machine learning for beginners.It is a mobile App that can help anyone Master AI & Machine Learning on the phone!Download it FROM APPLE APP STORE and conquer any skill level with interactive quizzes, certification exams, & animated concept maps in:Artificial IntelligenceMachine LearningDeep LearningGenerative AILLMsNLPxAIData ScienceAI and ML OptimizationAI Ethics & Bias ⚖️& more! ➡️ App Store Link

Imagine standing at a crossroads, juggling countless possibilities yet needing to choose just one path.That's what most early-stage founders struggle with. And for me, that's picking the right course towards the ever-elusive Product-Market fit.Today, I'll share how I tackle this challenge and what I do to show my best customers the highest possible value of my product as early as possible.This episode is sponsored by Paddle.com — if you're looking for a payment platform that works for you so you can focus on what matters, check them out.The blog post: https://thebootstrappedfounder.com/product-market-fit-time-to-first-value/The podcast episode: https://tbf.fm/episodes/360-product-market-fit-time-to-first-valueCheck out Podscan to get alerts when you're mentioned on podcasts: https://podscan.fmSend me a voicemail on Podline: https://podline.fm/arvidYou'll find my weekly article on my blog: https://thebootstrappedfounder.comPodcast: https://thebootstrappedfounder.com/podcastNewsletter: https://thebootstrappedfounder.com/newsletterMy book Zero to Sold: https://zerotosold.com/My book The Embedded Entrepreneur: https://embeddedentrepreneur.com/My course Find Your Following: https://findyourfollowing.comHere are a few tools I use. Using my affiliate links will support my work at no additional cost to you.- Notion (which I use to organize, write, coordinate, and archive my podcast + newsletter): https://affiliate.notion.so/465mv1536drx- Riverside.fm (that's what I recorded this episode with): https://riverside.fm/?via=arvid- TweetHunter (for speedy scheduling and writing Tweets): http://tweethunter.io/?via=arvid- HypeFury (for massive Twitter analytics and scheduling): https://hypefury.com/?via=arvid60- AudioPen (for taking voice notes and getting amazing summaries): https://audiopen.ai/?aff=PXErZ- Descript (for word-based video editing, subtitles, and clips): https://www.descript.com/?lmref=3cf39Q- ConvertKit (for email lists, newsletters, even finding sponsors): https://convertkit.com?lmref=bN9CZw

A Daily Chronicle of AI Innovations on December 05th 2024

A Daily Chronicle of AI Innovations on December 04th 2024

A Daily Chronicle of AI Innovations on December 03rd 2024

A Daily Chronicle of AI Innovations on December 02nd 2024

This Weekly in AI: Nov 23- Nov 30th 2024

Stephen Solka, CTO and co-founder of Standd.io, joins Elixir Wizards Owen and Charles to share the journey of building an AI-native deal intelligence and due diligence platform. Designed to streamline document analysis and text generation for venture capital firms, Standd.io leverages large language models and AI tools to address key customer pain points in document workflows. Stephen explains how Elixir and Phoenix LiveView enabled rapid UI iteration and seamless integration between the front-end and back-end. The conversation also explores the human side of startup life. Stephen reflects on balancing tech debt with customer demands, the value of accelerators in building networks and securing funding, and the challenges of pricing in early-stage startups. He emphasizes the importance of validating ideas with potential customers and learning from the hurdles of growing a business. Tune in for insights on leveraging AI in Elixir, solving real-world problems, and navigating the journey from concept to company. Topics discussed in this episode: The journey from self-taught programmer to CTO The perks of Phoenix LiveView for rapid UI development Integrating front-end and back-end technologies AI tools for code generation How early adopters balance functionality with product polish Validating ideas and understanding customer needs The impact of accelerators on networking and fundraising Approaches to managing pricing strategies for startups Balancing technical debt with feature development The role of telemetry and error reporting in product development Creating collaborative and supportive tech communities Educating users on AI's capabilities and limitations The broader implications of AI tools across industries Links Mentioned Contact Stephen & Julie at Standd: founders@standd.io https://www.standd.io/ https://www.digitalocean.com/community/tutorials/gangs-of-four-gof-design-patterns https://www.thriftbooks.com/w/code-completesteve-mcconnell/248753/item/15057346/ https://aws.amazon.com/sagemaker/ https://www.anthropic.com/ https://getoban.pro/ https://kubernetes.io/ https://www.apollographql.com/ https://aws.amazon.com/startups/accelerators https://accelerate.techstars.com/ https://aider.chat/ https://github.com/Aider-AI/aider https://neovim.io/ https://ui.shadcn.com/ https://tailwindui.com/ https://www.ycombinator.com/ https://www.thriftbooks.com/w/close-to-the-machine-technophilia-and-its-discontentsellen-ullman/392556 Special Guest: Stephen Solka.

Co-hosts Mark Thompson and Steve Little discuss how Anthropic's Claude 3.5 Sonnet upgrade has made Claude an even better AI writer. This make's it even easier to write a great research report. They move on to explore OpenAI's new desktop apps and Advanced Voice Mode, discussing how voice interaction could transform genealogical research, particularly for those who prefer speaking to typing. Combining search and AI in the same tool will be a huge timesaver for genealogy research. This week's Tip of the Week reveals the best ways to leverage free AI tools for family history research. There's no need to pay for AI tools if you only need these features a few times a day.In RapidFire, they examine the future of AI agents, Apple's cautious AI rollout in iOS 18.1, and Meta's strategic content partnership with Reuters.Timestamps:In the News:02:20  Claude 3.5 Sonnet (new): AI Writing Reaches New Heights06:50 ChatGPT Desktop Apps: Now Available for Both Macs and PCs14:00 AI Search Gets Real: AI-Enhanced Search From ChatGPTTip of the Week:19:20 Free AI Tools: How To Get Premium AI Results For FreeRapidFire:26:50 AI Agents: The Future of Computer Interaction31:20 Apple Intelligence: IOS 18.1 Starts the Rollout of AI for Apple40:26 Meta's Reuters Deal: The First (of Many?) Content Partnerships for FacebookResource Links:ANTHROPICClaude 3.5 Sonnethttps://www.anthropic.com/claude/sonnetProjects featurehttps://www.anthropic.com/news/projectsOPENAIChatGPThttps://chatgpt.com/Desktop Appshttps://openai.com/chatgpt/desktop/Advanced Voice Modehttps://help.openai.com/en/articles/8400625-voice-mode-faqGPT Searchhttps://openai.com/index/introducing-chatgpt-search/APPLEiOS 18.1https://www.apple.com/ca/newsroom/2024/10/apple-intelligence-is-available-today-on-iphone-ipad-and-mac/Apple Intelligencehttps://www.apple.com/apple-intelligence/Sirihttps://www.apple.com/siri/Apple Photo Searchhttps://9to5mac.com/2024/09/25/photos-search-in-ios-181-actually-works-thanks-to-apple-intelligence/METAMeta AIhttps://ai.meta.com/meta-ai/Reuters Partnershiphttps://www.reuters.com/technology/artificial-intelligence/meta-platforms-use-reuters-news-content-ai-chatbot-2024-10-25/MISCPerplexityhttps://www.perplexity.aiMicrosoft Copilothttps://www.microsoft.com/microsoft-365/copilotTags:Artificial Intelligence, Technology, Cloud Computing, Machine Learning, Family History, Genealogy, AI, Mobile Technology, Natural Language Processing, Large Language Models, Generative AI, AI Search, Voice Assistants, AI Agents, Research Tools, Content Creation, Digital Writing, Document Analysis, Privacy, Data Protection, AI Ethics, Claude 3.5 Sonnet, ChatGPT, Apple Intelligence, Meta AI, Perplexity, Microsoft Copilot, iOS 18.1, Siri, AI Writing, Desktop Applications, Mobile Apps, Photo Organization, Email Tools, Content Partnerships, Source Citation, Voice Interaction, User Experience, Cloud Services, Free Tools, Premium Features, Tool Comparison, Workflow Optimization, Family Research, Genealogy Tools, Research Reports, Narrative Writing, Genealogists, Family Historians, Tech Writers, Researchers, Digital Creators

Angel Vossough, the brains behind BetterAI, is shaking up the AI game by focusing on making wine searches and recommendations more personalized through her cool project, VinoVoss. Angel and the team blend their skills and vast wine knowledge to craft an AI solution that opens the gateway for everyone to become a better wine expert as well as guide choices to lesser known quality brands. A big advocate for diversity and inclusivity, Angel also discusses the significance of diverse AI training data and backs initiatives like "returnship" to empower women in tech, ensuring their perspectives shape more effective AI systems. So pour your favorite red, white or sparkling beverage and take in this episode. Highlights of our conversation: - BetterAI is transforming the wine industry by using data science and AI to simplify wine selection through their VinoVoss project, ensuring a comprehensive and unbiased shopping experience for consumers. - The Smart Sommelier feature partners with wine shops to offer personalized wine recommendations through conversational AI, allowing customers to make purchases directly through the platform for a seamless experience. - Angel stresses the importance of diverse perspectives, including women's opinions, in training data for AI systems to create inclusive and effective solutions that cater to diverse consumer needs. - BetterAI's focus on high-quality data is evident in their manual review process involving 37,000 wine experts, ensuring precise recommendations and simplifying the wine selection process for consumers. Angel Vossough is the CEO and Co-Founder of BetterAI, a Silicon Valley-based AI service provider headquartered in Silicon Valley. The company is uniquely leveraging advanced AI technologies such as Machine Learning, Generative AI, Natural Language Processing, and Computer Vision to create this transformative solution that is revolutionizing the relationship between wine and digital platforms. She is also Co-Founder & Managing Partner at Caspian Capital, an early-stage investment firm focusing on deep tech, biotech, and AI; and was Co- Founder of OpenCovidScreen, a non-profit focused on driving innovation in low-cost, accessible COVID-19 testing. In her role as BetterAI CEO, and with a strategic focus on VinoVoss as one of its primary products, Angel oversees the direction and growth of the company's innovative AI applications in the wine industry. This includes setting the overall strategic direction for BetterAI and VinoVoss; ensuring company objectives align with market needs and her company's vision; building and maintaining relationships with key stakeholders, partners, and investors to support and advance BetterAI's business goals; and ensuring the alignment of VinoVoss's development with BetterAI's broader technological advancements and business strategy. Connect with Angel: LinkedIn: https://www.linkedin.com/in/vossough/ Website: http://www.vinovoss.com Website: https://www.betterai.io Connect with Allison: Feedspot has named Disruptive CEO Nation as one of the Top 25 CEO Podcasts on the web and it is ranked the number 10 CEO podcast to listen to in 2024! https://podcasts.feedspot.com/ceo_podcasts/ LinkedIn: https://www.linkedin.com/in/allisonsummerschicago/ Website: https://www.disruptiveceonation.com/ Twitter: @DisruptiveCEO #CEO #brand #startup #startupstory #founder #business #businesspodcast #podcast Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of RAPM Focus, Editor-in-Chief Brian Sites, MD, is thrilled to welcome Laura Graham, PhD, MPH, and Sesh Mudumbai, MD, MS, following the April 2024 publication of their brief technical report, “Use of natural language processing method to identify regional anesthesia from clinical notes.” One definition of medicine is the science and practice of the diagnosis treatment in prevention of disease. Science itself involves diagnoses and relies on the process of assessing data to determine cause and effect in therapies. However, in the busy world of clinical productivity and limited resources, the science of medicine is often overlooked. Physicians struggle to extract meaningful data from electronic medical records, despite their great potential. This is often due to the prioritization of funding for billing and compliance, which leads to challenges in accessing meaningful data. Additionally, barriers such as data license agreements and institutional review board considerations further complicate matters. This is why Dr. Sites is excited about new technologies, such as artificial intelligence that can assist physicians in the practice in the science of medicine. Dr. Laura Graham is an epidemiologist with VA's Health Economics Resource Center at the VA Palo Alto Health Care System and an associate faculty with the Stanford-Surgery, Policy, Improvement Research, and Education Center at the Stanford University School of Medicine. Her research interests include causal inference methods and improving clinical processes of care for surgery. Dr. Sesh Mudumbai is an associate professor in the Department of Anesthesiology, Perioperative, and Pain Medicine at Stanford University School of Medicine and a staff anesthesiologist at the VA Palo Alto Health Care System. His research interests include using and developing informatics tools to improve opioid management and perioperative outcomes. *The purpose of this podcast is to educate and to inform. The content of this podcast does not constitute medical advice, and it is not intended to function as a substitute for a healthcare practitioner's judgement, patient care, or treatment. The views expressed by contributors are those of the speakers. BMJ does not endorse any views or recommendations discussed or expressed on this podcast. Listeners should also be aware that professionals in the field may have different opinions. By listening to this podcast, listeners agree not to use its content as the basis for their own medical treatment or for the medical treatment of others. Podcast and music produced by Dan Langa. Find us on X @RAPMOnline, Facebook @Regional Anesthesia & Pain Medicine, and Instagram @RAPM_Online.

Zukunftstrends KI 2025 Shownotes In Episode 867 von TomsTalkTime – DER Erfolgspodcast tauchen wir tief in die Zukunft der KI ein und erkunden die wichtigsten Trends für 2025. Erfahre, wie du KI-gestützte Innovationen optimal für dein Unternehmen nutzen kannst, um an der Spitze zu bleiben und von Best Practices erfolgreicher Unternehmen zu lernen. Diese Episode bietet dir wertvolle Insights, um deine Strategie für das kommende Jahr KI-fit zu machen. Zusammenfassung und Stichpunkte In dieser Episode sprechen wir über: Warum KI-basierte Innovationen/ Zukunftstrends KI 2025 entscheidend sind: In einem sich schnell entwickelnden Marktumfeld sind KI-Innovationen ein Schlüsselfaktor, um flexibel und wettbewerbsfähig zu bleiben. Die bedeutendsten Zukunftstrends KI 2025: Von maschinellem Lernen über Natural Language Processing bis hin zu Fortschritten in der Bild- und Spracherkennung. Strategien zur Integration von KI in Unternehmensprozesse: Wie du KI schrittweise in bestehende Abläufe einbindest und dabei dein Team erfolgreich einbeziehst. Best Practices von Unternehmen: Wie Amazon und andere Marktführer KI bereits effektiv nutzen, um innovativ und kundenorientiert zu agieren. Diese und weitere Themen helfen dir dabei, den Wettbewerbsvorteil von KI zu verstehen und für dein Unternehmen nutzbar zu machen. Zukunftstrends KI 2025 - In dieser Episode geht es darum, wie Unternehmen die neuesten Entwicklungen und Innovationen in der Künstlichen Intelligenz optimal für sich nutzen können, um erfolgreich zu bleiben. Die Episode liefert nicht nur einen Überblick über die neuesten Technologien, sondern zeigt auch konkrete Schritte, wie man KI effektiv in Unternehmensprozesse integrieren kann. Warum KI-gestützte Innovationen entscheidend sind: Der Einsatz von KI wird für Unternehmen immer wichtiger, um den steigenden Erwartungen der Kunden gerecht zu werden und sich gegen die Konkurrenz zu behaupten. KI bietet einzigartige Möglichkeiten, um Märkte in Echtzeit zu analysieren und auf Trends blitzschnell zu reagieren. Diese Vorteile sind besonders für Unternehmen entscheidend, die eine starke Position im Wettbewerb halten oder ausbauen möchten. Wichtige KI-Technologien, die 2025 die Unternehmenslandschaft prägen werden: Im Fokus stehen vor allem maschinelles Lernen und Natural Language Processing (NLP), die es ermöglichen, Prozesse zu automatisieren und riesige Datenmengen effizient auszuwerten. Diese Technologien bieten insbesondere im Kundenservice und der Produktentwicklung große Potenziale. Durch Fortschritte in der Bild- und Spracherkennung wird es immer einfacher, innovative, kundenorientierte Produkte zu entwickeln. Strategien zur Integration von Zukunftstrends KI 2025 in bestehende Prozesse: Wer KI erfolgreich einführen möchte, sollte mit kleinen Schritten beginnen. Oft ist es sinnvoll, mit Bereichen wie Kundensupport oder Datenanalyse zu starten. Die Integration sollte in Etappen erfolgen, und es ist wichtig, das Team frühzeitig einzubinden, um Unterstützung und kreative Ideen aus dem eigenen Unternehmen zu gewinnen. Best Practices erfolgreicher Unternehmen: In der Episode schauen wir uns Beispiele von Unternehmen wie Amazon an, die KI nutzen, um Prozesse zu optimieren und die Kundenerfahrung zu verbessern. Vom Logistikmanagement bis hin zur automatisierten Kundenbetreuung setzen diese Unternehmen KI-Technologien gezielt ein, um innovativ und wettbewerbsfähig zu bleiben. Durch datenbasierte Entscheidungen und gezielte Investitionen in neue Technologien sichern sich diese Unternehmen ihren Vorsprung am Markt. Diese Episode bietet dir die wichtigsten Einblicke und Tipps, wie du KI-Zukunftstrends nutzen kannst, um dein Unternehmen erfolgreich zu positionieren. Egal, ob du neu in der KI bist oder bereits Erfahrung hast – hier gibt's wertvolle Inspiration und praktische Ansätze, um 2024 zu einem starken Jahr für dein Business zu machen! Und denk immer daran: Wer will, findet Wege. Wer nicht will, findet Gründe. Tschüss, mach's gut. Dein Tom.   Hol Dir jetzt Dein Hörbuch "Selfmade Millionäre packen aus" und klicke auf das Bild! Buchempfehlung bei Amazon: Denken Sie wie Ihre Kunden   +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Mehr Freiheit, mehr Geld und mehr Spaß mit DEINEM eigenen Podcast. Erfahre jetzt, warum es auch für Dich Sinn macht, Deinen eigenen Podcast zu starten. Jetzt hier zum kostenlosen Podcast-Workshop anmelden: https://Podcastkurs.com +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++   So fing alles an. Hier geht´s zur allerersten Episode von TomsTalkTime.com – DER Erfolgspodcast. Und ja, der Qualitätsunterschied sollte zu hören sein. Aber hey, das war 2012…

I have another amazing CRE guest using AI to help real estate pros up their game to introduce you to this week, Thomas Foley, co-founder and CEO of Archer.re   If you're tired of the long, slow grind of underwriting deals, you'll love this episode.   Archer.re is leveraging AI in the deal underwriting process, taking it from taking hours down to just a few minutes.   Imagine pulling in all your data, dropping it into your model (yes, your own model – no forced software here), and watching Archer make it happen.   Whether you're in multifamily or another asset class, Thomas's platform will open your eyes to what's possible.   Plus, you'll see how Archer is using AI to automate not just underwriting but also deal sourcing.   Picture this: predictive factors alerting you when an off-market property is likely to hit the market before it's listed.   Skeptical? Well, see how Archer has clients like Marcus & Millichap and Starwood using the platform.   Here's what Archer.re helps you do: Data-Driven Insights: Access deep, AI-powered analysis for smart property and investment decisions. Market Trends & Predictions: Stay ahead of trends with advanced forecasting for commercial real estate (CRE) markets. Automated Valuations: Get instant, accurate valuations on target assets, saving you time and increasing precision. Portfolio Optimization: Manage, track, and optimize your own assets in real time. Streamlined Decision-Making: Make faster, data-backed investment decisions with customizable reports and real-time analytics. Archer is another company that will cause you to rethink everything you know about deal analysis – and help you find, evaluate, and buy deals before they even come to market. ***** The only Podcast you need on for raising capital in real estate - enhanced by AI.   Learn how other real estate pros are using AI to get ahead of their competition.   Get early notice of hot new game-changing AI real estate apps.   Walk away with something you can actually use in every episode.   PLUS, subscribe to my free newsletter and get: • practical guides, • how-to's, and • news updates Get immediately actionable strategies and tools to raise more capital, connect with accredited investors, and scale faster—enhanced by AI to stay ahead of the competition. All in just 5 minutes, for free. https://gowercrowd.com/subscribe

Send us a textEver wondered how your favorite virtual assistant seems to understand you almost like a human friend? Uncover the magic behind Large Language Models (LLMs) as we embark on an exciting journey into the world of artificial intelligence in our latest episode of the AI for Kids podcast. We'll explore how these powerful models learn from vast amounts of text to generate human-like responses, making everyday tech like translation apps and chatbots feel almost magical. Through fun language games and engaging stories, we explain how LLMs predict and create text, transforming communication and learning in fascinating ways.Get ready for a playful adventure that brings AI to life with relatable examples and insights kids and parents will love. While these models are truly impressive, we'll also tackle the challenges they face, like occasionally getting things wrong or needing enormous amounts of data. We encourage curiosity by exploring how you can engage with chatbots and discover kid-friendly videos that showcase their capabilities. Whether you're a young tech enthusiast or a curious parent, this episode promises a delightful exploration of how LLMs and AI are shaping the future of communication and education.Resources:Google AI MachineTeachable Machine#LargeLanguageModels #LLM #LanguageModels #GenerativeAI #LLMInnovation #AITextGeneration #LLMResearch #FutureOfLanguageModels #AIandLanguage #TextGenerationAI #AdvancedLanguageModels #NaturalLanguageModels #LLMApplications #LLMDevelopment #AIText #LLMExplained #LargeLanguageModelSupport the showHelp me become the #1 (number one) podcast and podcaster for AI for Kids. Social Media & Contact: Website: www.aidigitales.com Email: contact@aidigitales.com Follow Us: Instagram, YouTube Gift or get our books on Amazon or Free AI Worksheets Listen, rate, and subscribe! Stay updated with our latest episodes by subscribing to AI for Kids on your favorite podcast platform. Apple Podcasts Amazon Music Spotify YouTube Other Like our content, subscribe or feel free to donate to our Patreon here: patreon.com/AiDigiTales

The AI Dream Team: Strategies for ML Recruitment and Growth // MLOps Podcast #267 with Jelmer Borst, Analytics & Machine Learning Domain Lead, and Daniela Solis, Machine Learning Product Owner, of Picnic. // Abstract Like many companies, Picnic started out with a small, central data science team. As this grows larger, focussing on more complex models, it questions the skillsets & organisational set up. Use an ML platform, or build ourselves? A central team vs. embedded? Hire data scientists vs. ML engineers vs. MLOps engineers How to foster a team culture of end-to-end ownership How to balance short-term & long-term impact // Bio Jelmer Borst Jelmer leads the analytics & machine learning teams at Picnic, an app-only online groceries company based in The Netherlands. Whilst his background is in aerospace engineering, he was looking for something faster-paced and found that at Picnic. He loves the intersection of solving business challenges using technology & data. In his free time loves to cook food and tinker with the latest AI developments. Daniela Solis Morales As a Machine Learning Lead at Picnic, I am responsible for ensuring the success of end-to-end Machine Learning systems. My work involves bringing models into production across various domains, including Personalization, Fraud Detection, and Natural Language Processing. // MLOps Jobs board https://mlops.pallet.xyz/jobs // MLOps Swag/Merch https://mlops-community.myshopify.com/ // Related Links Website: --------------- ✌️Connect With Us ✌️ ------------- Join our slack community: https://go.mlops.community/slack Follow us on Twitter: @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Catch all episodes, blogs, newsletters, and more: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Jelmer on LinkedIn: https://www.linkedin.com/in/japborst Connect with Daniela on LinkedIn: https://www.linkedin.com/in/daniela-solis-morales/

The internet is now, decidedly, unsafe for kids...including A.I. generated platforms. At Angel Kids AI, their mission is to use AI to flip the script; making the internet safe for kids.  I brought in Tim Estes, founder of Angel Kids AI, to educate parents about A.I., the harms, the potential and how to introduce our kids to A.I. tools safely and productively. Tim and I dig into online harms, reviewing popular AI products like Amazon's Alexa, ChatGPT and others. Parents will leave this conversation with a healthy understanding of how to introduce AI to our kids in a way that enhances their creativity and doesn't harm them.  In addition to joining the waitlist for Angel Kids AI, here are a few AI tools that Tim recommends trying out with your child: Merlyn Mind Khan Academy Synthesis About Tim: Tim Estes is a pioneering executive in the Artificial Intelligence and Natural Language Processing domain. He founded and led Digital Reasoning from 2000, an AI leader in the space of unstructured data analytics for 20+ years. Estes envisioned a means by which computers could learn to accurately interpret language, understand context, and extract critical intelligence. Digital Reasoning secured investments from In-Q-Tel (IQT), Goldman Sachs, Nasdaq, BNP Paribas, Barclays, Credit Suisse, HCA, and others totaling over $120M. Dubbed “Wall Street's Robocop” by Forbes, the technology in Digital Reasoning became the market standard for applying AI and Natural Language Processing to communications data across text and voice and was adopted by a majority of the Tier 1 Financial Institutions around the globe. It's technology also supported key Defense and Intelligence efforts in the US Government, enabled nationwide cancer screening systems to automate patient detection and prioritization, and created the largest deployed system to support law enforcement in the disruption of child sex trafficking rings in North America. It was merged with Smarsh in November, 2020 to create the market leader in Communications Intelligence for Financial Services. Estes left the acquiring company, Smarsh, at the end of 2021 and is now an active Angel Investor, Advisor, Board Member, and Mentor to CEOs in multiple industries. --- Support this podcast: https://podcasters.spotify.com/pod/show/scrolling2death/support

In today's Mid Mic Daily Bite, AI-generated versions of your favorite hosts, Bunchu and Chamber, take you on an educational journey through the ever-expanding world of artificial intelligence. This episode is all about decoding the most essential and commonly used terms in AI. Have you ever wondered what phrases like "machine learning," "neural networks," or "natural language processing" really mean? We've got you covered!Bunchu and Chamber break down these complex concepts into simple, bite-sized explanations, showing how they impact your daily life and the tech you use every day. Whether you're new to AI or just looking to brush up on your knowledge, this episode serves as your go-to guide for understanding the key terms shaping the future of technology. With humor and clarity, our hosts demystify the language of AI, making it accessible for everyone.Stay tuned to learn how AI is integrated into the gadgets you use, the apps you rely on, and even the future of the workforce. Join us as we decode the buzzwords driving the AI revolution—it's your crash course in the AI glossary!Follow Us:Website: https://midmiccrisis.com/  YouTube: https://www.youtube.com/@midmiccrisisInstagram: https://www.instagram.com/midmiccrisis/?hl=enTikTok: https://www.tiktok.com/@mid.mic.crisis?lang=enTwitter: https://twitter.com/MidMicCrisisNewsletter: https://mid-mic-crisis-newsletter.beehiiv.com/subscribeMMC Push Pass: https://ks-pages-119byl.web.app/pass/66db3c111db9a79db7fdaafeFireBrain AI: https://www.skool.com/firebrainPowered by @dGenNetworkWebsite: https://dgen.network/Support the show

AI might soon be making your wine selections, even at sports events. Angel Vossough, CEO & Co-Founder Angel Vossough is the CEO and Co-Founder of BetterAI, a Silicon Valley-based AI service provider headquartered in Silicon Valley. The company is uniquely leveraging advanced AI technologies such as Machine Learning, Generative AI, Natural Language Processing, and Computer … Continue reading Angel Vossough, VinoVoss and AI Wine →

In this week's episode, Will & Jill discuss Natural Language Processing (NLP), explaining its goals, applications, and challenges. They break down NLP into subtopics, covering its definition, how it works, and different use cases such as chatbots, virtual assistants, and transcription software. They also touch on the challenges NLP faces, including understanding context, idiomatic expressions, and diversity in language. ___Connect with JillConnect with Will___160 Characters is powered by Clerk Chat.

 Connect with Jill.Connect with Will.___160 Characters is powered by Clerk Chat.

Guest: Sander Schulhoff, CEO and Co-Founder, Learn Prompting [@learnprompting]On LinkedIn | https://www.linkedin.com/in/sander-schulhoff/____________________________Host: Sean Martin, Co-Founder at ITSPmagazine [@ITSPmagazine] and Host of Redefining CyberSecurity Podcast [@RedefiningCyber]On ITSPmagazine | https://www.itspmagazine.com/sean-martinView This Show's Sponsors___________________________Episode NotesIn this episode of Redefining CyberSecurity, host Sean Martin engages with Sander Schulhoff, CEO and Co-Founder of Learn Prompting and a researcher at the University of Maryland. The discussion focuses on the critical intersection of artificial intelligence (AI) and cybersecurity, particularly the role of prompt engineering in the evolving AI landscape. Schulhoff's extensive work in natural language processing (NLP) and deep reinforcement learning provides a robust foundation for this insightful conversation.Prompt engineering, a vital part of AI research and development, involves creating effective input prompts that guide AI models to produce desired outputs. Schulhoff explains that the diversity of prompt techniques is vast and includes methods like the chain of thought, which helps AI articulate its reasoning steps to solve complex problems. However, the conversation highlights that there are significant security concerns that accompany these techniques.One such concern is the vulnerability of systems when they integrate user-generated prompts with AI models, especially those prompts that can execute code or interact with external databases. Security flaws can arise when these systems are not adequately sandboxed or otherwise protected, as demonstrated by Schulhoff through real-world examples like MathGPT, a tool that was exploited to run arbitrary code by injecting malicious prompts into the AI's input.Schulhoff's insights into the AI Village at DEF CON underline the community's nascent but growing focus on AI security. He notes an intriguing pattern: many participants in AI-specific red teaming events were beginners, which suggests a gap in traditional red teamer familiarity with AI systems. This gap necessitates targeted education and training, something Schulhoff is actively pursuing through initiatives at Learn Prompting.The discussion also covers the importance of studying and understanding the potential risks posed by AI models in business applications. With AI increasingly integrated into various sectors, including security, the stakes for anticipating and mitigating risks are high. Schulhoff mentions that his team is working on Hack A Prompt, a global prompt injection competition aimed at crowdsourcing diverse attack strategies. This initiative not only helps model developers understand potential vulnerabilities but also furthers the collective knowledge base necessary for building more secure AI systems.As AI continues to intersect with various business processes and applications, the role of security becomes paramount. This episode underscores the need for collaboration between prompt engineers, security professionals, and organizations at large to ensure that AI advancements are accompanied by robust, proactive security measures. By fostering awareness and education, and through collaborative competitions like Hack A Prompt, the community can better prepare for the multifaceted challenges that AI security presents.Top Questions AddressedWhat are the key security concerns associated with prompt engineering?How can organizations ensure the security of AI systems that integrate user-generated prompts?What steps can be taken to bridge the knowledge gap in AI security among traditional security professionals?___________________________SponsorsImperva: https://itspm.ag/imperva277117988LevelBlue: https://itspm.ag/attcybersecurity-3jdk3___________________________Watch this and other videos on ITSPmagazine's YouTube ChannelRedefining CyberSecurity Podcast with Sean Martin, CISSP playlist:

Machine learning, natural language processing, rule-based systems, neural networks, deep learning…these are all terms used in the artificial intelligence space.  But what does it all mean? Dr. Serina Chang knows a thing or two about this AI stuff and joins the Public Health Insight Podcast for a deep dive into the basics of AI as well as some key concepts to lay the foundational knowledge required to apply computer science in a population health context.  Guest◼️ Serina Chang, PhDHost ◼️ Gordon Thane, BMSc, MPH, PMP®Producer(s)◼️ Gordon Thane, BMSc, MPH, PMP®◼️ Leshawn Benedict, MPH, MSc, PMP®Production Notes◼️ Music from Johnny Harris x Tom Fox: The Music RoomSubscribe to the NewsletterSubscribe to The Insight newsletter so you don't miss out on the latest podcast episodes, live events, job skills, learning opportunities, and other engaging professional development content here.Leave Us Some FeedbackIf you enjoy our podcasts, be sure to subscribe and leave us a rating on Apple Podcast or Spotify, and spread the word to your friends to help us get discovered by more people. You can also interact directly with the podcast episodes on Spotify using the new “comment” feature! We'd love to hear what you think.Send us a Text Message to let us know what you think.

Hugo speaks with Ines Montani and Matthew Honnibal, the creators of spaCy and founders of Explosion AI. Collectively, they've had a huge impact on the fields of industrial natural language processing (NLP), ML, and AI through their widely-used open-source library spaCy and their innovative annotation tool Prodigy. These tools have become essential for many data scientists and NLP practitioners in industry and academia alike. In this wide-ranging discussion, we dive into: • The evolution of applied NLP and its role in industry • The balance between large language models and smaller, specialized models • Human-in-the-loop distillation for creating faster, more data-private AI systems • The challenges and opportunities in NLP, including modularity, transparency, and privacy • The future of AI and software development • The potential impact of AI regulation on innovation and competition We also touch on their recent transition back to a smaller, more independent-minded company structure and the lessons learned from their journey in the AI startup world. Ines and Matt offer invaluable insights for data scientists, machine learning practitioners, and anyone interested in the practical applications of AI. They share their thoughts on how to approach NLP projects, the importance of data quality, and the role of open-source in advancing the field. Whether you're a seasoned NLP practitioner or just getting started with AI, this episode offers a wealth of knowledge from two of the field's most respected figures. Join us for a discussion that explores the current landscape of AI development, with insights that bridge the gap between cutting-edge research and real-world applications. LINKS The livestream on YouTube (https://youtube.com/live/-6o5-3cP0ik?feature=share) How S&P Global is making markets more transparent with NLP, spaCy and Prodigy (https://explosion.ai/blog/sp-global-commodities) A practical guide to human-in-the-loop distillation (https://explosion.ai/blog/human-in-the-loop-distillation) Laws of Tech: Commoditize Your Complement (https://gwern.net/complement) spaCy: Industrial-Strength Natural Language Processing (https://spacy.io/) LLMs with spaCy (https://spacy.io/usage/large-language-models) Explosion, building developer tools for AI, Machine Learning and Natural Language Processing (https://explosion.ai/) Back to our roots: Company update and future plans, by Matt and Ines (https://explosion.ai/blog/back-to-our-roots-company-update) Matt's detailed blog post: back to our roots (https://honnibal.dev/blog/back-to-our-roots) Ines on twitter (https://x.com/_inesmontani) Matt on twitter (https://x.com/honnibal) Vanishing Gradients on Twitter (https://twitter.com/vanishingdata) Hugo on Twitter (https://twitter.com/hugobowne) Check out and subcribe to our lu.ma calendar (https://lu.ma/calendar/cal-8ImWFDQ3IEIxNWk) for upcoming livestreams!

Artificial Intelligence (AI) is transforming the financial industry, offering both exciting opportunities and significant challenges.How is AI being integrated specifically into financial services? What are the potential risks and rewards of this technology?In this episode, Ryan Detrick, Chief Market Strategist at Carson Group & Sonu Varghese, VP, Global Macro Strategist at Carson Group, sit down with Davi Fava, Chief Strategy Officer at Carson Group, for an in-depth conversation on artificial intelligence and its revolutionary impact on finance. Dani takes us through the intricate world of AI, explaining its incredible potential, addressing common fears, and highlighting how Carson Group is integrating this technology to better serve their clients.Additionally to our conversation with Dani, Ryan and Sonu discuss the recent performance of the S&P 500, the impact of economic data on market trends, and the outlook for future market movements. They also touch on the role of small businesses and the Federal Reserve's actions in shaping the economy.Key topics discussed:New ways AI is transforming industries, especially financeAddressing fears and ethical considerations surrounding AIPractical applications of AI in daily financial operations and beyondThe recent performance of the S&P 500, including the longest win streak in 20 yearsThe impact of economic data, such as unemployment claims, on market trendsThe Federal Reserve's actions and potential rate cutsAnd more!Resources:Any questions about the show? Send it to us! We'd love to hear from you! factsvsfeelings@carsongroup.com Connect with Davi Fava: LinkedIn: Davi FavaConnect with Ryan Detrick: LinkedIn: Ryan DetrickX: Ryan DetrickConnect with Sonu Varghese: LinkedIn: Sonu VargheseX: Sonu VargheseAbout Our Guest:Dani Fava serves the pivotal role of Chief Strategy Officer at Carson Group, wielding over two decades of experience in wealth management to drive our strategic direction. Charged with enhancing the client experience through differentiated products and services, Dani's leadership is instrumental in shaping innovative growth strategies and advancing the industry on financial advice and the use of AI.Before joining Carson Group, Dani held influential positions at Envestnet and TD Ameritrade, where she spearheaded initiatives such as digital onboarding, strategic partnerships, and AI integration. Dani's contributions include the development of advisor technologies like iRebal and the Model Market Center, reflecting her commitment to empowering advisors with cutting-edge tools and insights. Her impact and forward-thinking vision earned her a spot on FinTech Magazine's Top 100 Women in FinTech (2021), InvestmentNews' Women to Watch (2020), and ThinkAdvisor's 2020 IA25, underscoring her status as a trailblazer in wealth management innovation.In addition to her professional achievements, Dani is recognized for her thought leadership in the industry, often speaking at conferences and contributing to discussions on the future of financial technology. Dani also serves on the board of Language & Culture Worldwide (LCW), a culture consulting company. This work is an extension of her commitment to see more inclusion in the wealth management industry.

On our final episode this season of Working Smarter we talk to Sophia Wang, an assistant professor of ophthalmology at Stanford University. Wang leads the school's ophthalmic informatics and artificial intelligence group, which uses the latest machine learning techniques to analyze electronic health records. In practice, that means looking at disparate sources of data—from doctors' notes and eye exam data to diagnostic imagery and billing codes—and finding the sorts of patterns that can be difficult for humans to spot.Hear Wang talk about using AI to extract useful information from a sea of unstructured data, and how to make better decisions with the data you already have—which, in Wang's case, means improving outcomes for glaucoma patients and providing a better quality of care.Show notes:Learn more about Sophia and her researchVisit Stanford University's Ophthalmic Informatics and Artificial Intelligence Group~ ~ ~Working Smarter is a new podcast from Dropbox about how AI is changing the way we work and get stuff done.You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard, namely: our producers Samiah Adams and Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to Benjy Baptiste for production assistance, our marketing and PR consultant Meggan Ellingboe, and our illustrators, Fanny Luor and Justin Tran. Our theme song was created by Doug Stuart. Working Smarter is hosted by Matthew Braga.

In today's episode, we'll hear from Paul Galloway on natural language processing. What is it, and how does it differ from machine learning? How is it used in finance and fraud detection? Tune in to find out. 

In this episode of the Eye on AI podcast, we explore the forefront of voice-powered AI technology with Trevor Back, Chief Product Officer at Speechmatics. Discover how Speechmatics is pushing the boundaries of speech recognition and conversational AI with their latest innovation, Flow.   Trevor shares his journey from a background in computational astrophysics to becoming a key figure in AI at DeepMind and now Speechmatics. He delves into the development and potential of Flow, a groundbreaking tool combining automatic speech recognition (ASR), large language models (LLMs), and text-to-speech synthesis, aimed at creating seamless and responsive voice interactions.   We explore the wide-ranging applications of Speechmatics' technology across industries, including media, call centers, and education. Trevor discusses the challenges of achieving high accuracy in speech recognition, especially in diverse and noisy environments, and how Speechmatics addresses these challenges with their unique approach to training models.   Listen in as we uncover the intricacies of handling multiple languages, improving diarization, and the future goals of understanding complex audio cues like emotion and sarcasm. Learn about the company's vision for integrating voice technology into everyday products, making technology more accessible and user-friendly.   Don't miss this insightful conversation on the future of voice technology, AI in business, and its role in the evolving landscape of AI. Like, subscribe, and hit the notification bell for more expert discussions on cutting-edge advancements in AI.     This episode is sponsored by Shopify. Shopify is a commerce platform that allows anyone to set up an online store and sell their products. Whether you're selling online, on social media, or in person, Shopify has you covered on every base. With Shopify you can sell physical and digital products. You can sell services, memberships, ticketed events, rentals and even classes and lessons. Sign up for a $1 per month trial period at http://shopify.com/eyeonai     Checkout Speechmatics, the most accurate AI speech technology - with AI transcription & real-time translation components.: https://www.speechmatics.com/     Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI     (00:00) Introduction and Background (01:49) Trevor Back's Journey into AI (04:02) DeepMind and Early AI Applications (07:30) Speechmatics' Mission and Focus (12:06) Key Applications of Speechmatics Technology (14:25) Achieving High Accuracy and Low Latency (17:52) Language Coverage and Challenges (21:27) Future of Voice Technology and AGI (24:52) Integrating Large Language Models (27:31) Handling Multiple Voices and Diarization (29:32) Real-world Applications and Challenges (35:20) Demonstration of Flow and Capabilities (41:14) Endpoint Prediction and Interruption (43:53) Real-time Interactions and Future Prospects (45:34) Launch Event and Future Plans (50:13) New Language Releases and Compliance

Angel Vossough is the CEO and Co-Founder of BetterAI, a Silicon Valley-based AI service provider headquartered in Silicon Valley. The company is uniquely leveraging advanced AI technologies such as Machine Learning, Generative AI, Natural Language Processing, and Computer Vision to create this transformative solution that is revolutionizing the relationship between wine and digital platforms. She is also Co-Founder & Managing Partner at Caspian Capital, an early-stage investment firm focusing on deep tech, biotech, and AI; and was Co-Founder of OpenCovidScreen, a non-profit focused on driving innovation in low-cost, accessible COVID-19 testing. Angel, an esteemed data scientist, holds dual bachelor's degrees in Mathematics and Computer Engineering, as well as master's degrees in Software Engineering from San Jose State University, and Data Science from UC Berkeley where she graduated with honors. Angel was previously a Senior Network Engineer at Cisco Systems, specializing in Network Architecture for major telecommunications companies including Verizon Wireless. BetterAI's products include “VinoVoss” (www.VinoVoss.com), a semantic search and recommendation system creating a virtual wine sommelier, and “BetterMed,” a generative AI-based medical diagnostic assistant. Angel is a technology leader and serial entrepreneur. She also founded DiverseUp, a public-benefit corporation building a professional community for technical & scientific women. In her role as BetterAI CEO, and with a strategic focus on VinoVoss as one of its primary products, Angel oversees the direction and growth of the company's innovative AI applications in the wine industry. This includes setting the overall strategic direction for BetterAI and VinoVoss; ensuring company objectives align with market needs and her company's vision; building and maintaining relationships with key stakeholders, partners, and investors to support and advance BetterAI's business goals; and ensuring the alignment of VinoVoss's development with BetterAI's broader technological advancements and business strategy. Listen to this informative Sharkpreneur episode with Angel Vossough about transforming wine selection with natural language processing. Here are some of the beneficial topics covered on this week's show: - How women's needs in the workplace are dynamic and change with different life stages. - How BetterAI aims to make win accessible to all by simplifying the wine selection process. - Why BetterAI focuses on specific datasets instead of using large language models. - How regulation, ethical AI, user privacy, and data privacy is vital in AI development. - How entrepreneurs should focus on solving real problems, hiring smarter people, and maintaining agility and flexibility. Connect with Angel: Guest Contact Info LinkedIn linkedin.com/company/betterai-io Links Mentioned: betterai.io Learn more about your ad choices. Visit megaphone.fm/adchoices

What a pleasure it was to speak with  L.D. Salmanson, Co-founder and CEO of Cherre, for this week's podcast.    Cherre is dramatically transforming real estate investment and management by leveraging cutting-edge data science and AI to consolidate massive amounts of data (including users' own data – it's NOT easy to do but Cherre have done it), so they can find opportunities across all aspects of their operations in seconds.   L.D.'s use case examples are crazy: ‘I am looking for this kind of asset, with these exact demographics, with these kinds of tax breaks, in this type of market' – and boom! out comes a selection for you to look at.   You won't believe what Cherre can do and, if you are anything like me, you'll want to set up an account as soon as you finish listening!   Alas, however, the tool is only being used currently by some of the top institutional investors but, L.D. assures me, making it available for mere mortals like thee and me is on their horizon.   Spend a few minutes tuning in to hear one of the foremost leaders in moving real estate into the future in this episode with L.D. Salmanson.   Cherre's core functions:   Building detailed asset models to reduce expenses while increases revenues. Creating sensitivity tables so investors can identify optimal investment (or divestment) strategies. Connecting disparate data with advanced tools to detect anomalies and find opportunities.   Key Points Discussed:   Advanced Tools: Data retrieval, integrity, conversational bots, GPT Omni model for omni-channel interactions. AI Capabilities: Vision, voice, text interpretation; future potential for smell interpretation. Platform Functions: Builds asset models, sensitivity tables; connects disparate data using entity resolution engines, knowledge graphs, machine learning, AI to detect anomalies. Data Processing & Visualization: Uses Snowflake, Google, Microsoft, Power Bi, Click, Tableau. Technological Highlights: Autoregressive models, attention mechanisms, large language models (LLMs) in natural language processing.   Don't miss L.D.'s insights on the inevitable integration of AI into real estate, the efficiencies it brings, and his use of GPT Omni to streamline presentation prep.   Cherre stands as a visionary leap in data-driven real estate management.   From L.D.   Why should real estate professionals be paying attention to AI today? AI is set to revolutionize real estate similarly to how it transformed financial services, automating intermediary processes and enhancing decision-making. Those who adapt early will gain significant advantages, while late adopters risk falling behind​​.   How do you use AI daily? What tools and apps do you like to use? I use GPT Omni extensively for preparing weekly company presentations, reducing preparation time from 8-10 hours to 2-3 hours. I also use Claude for interactive idea discussions and refining presentation content​​ .   Can you think of an easy win that somebody could do immediately after listening today? An easy win is building asset models with AI. By inputting an assumptions table into a model, AI can populate the inputs and create sensitivity tables, reducing manual errors and saving significant time​​. ***** The only Podcast you need on real estate and AI.   Learn how other real estate pros are using AI to get ahead of their competition.   Get early notice of hot new game-changing AI real estate apps.   Walk away with something you can actually use in every episode.   PLUS, subscribe to my free newsletter and get: • practical guides, • how-to's, and • news updates   All exclusively for real estate investors that make learning AI fun and easy and insanely productive, for free.   EasyWin.AI

In this episode, we're introducing a new format that we hope to revisit every few months. We're joined by HPE's CTO, Fidelma Russo, to discuss the rapid advancements in AI and the broader enterprise tech landscape for 2024. Fidelma shares her insights on the staggering growth in AI, the importance of data management, and the potential for AI to democratize technology and foster inclusivity. She also touches on the ethical considerations and the need for responsible AI implementation.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it.Do you have a question for the expert? Ask it here using this Google form: https://forms.gle/8vzFNnPa94awARHMAAbout this week's guest: https://www.hpe.com/uk/en/leadership-bios/fidelma-russo.htmlSources and statistics cited in this episode:- AI industry growth: Statista - https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide#:~:text=Artificial%20intelligence%20(AI)%20worldwide%20%2D%20statistics%20%26%20facts&text=The%20market%20for%20AI%20technologies%20is%20vast%2C%20amounting%20to%20around,trillion%20U.S.%20dollars%20by%202030 - Connectivity and networking industry size: Markets and Markets - https://www.marketsandmarkets.com/Market-Reports/wireless-connectivity-market-192605963.html - Security industry size: Grandview Research - https://www.grandviewresearch.com/industry-analysis/cyber-security-market - Storage industry size: Fortune Business Insights - https://www.fortunebusinessinsights.com/data-storage-market-102991- Green tech and Sustainability industry size: Fortune Business Insights - https://www.fortunebusinessinsights.com/green-technology-and-sustainability-market-102221Smartphone ‘X Ray' chip - https://ieeexplore.ieee.org/abstract/document/10381731

In this episode, we're introducing a new format that we hope to revisit every few months. We're joined by HPE's CTO, Fidelma Russo, to discuss the rapid advancements in AI and the broader enterprise tech landscape for 2024. Fidelma shares her insights on the staggering growth in AI, the importance of data management, and the potential for AI to democratize technology and foster inclusivity. She also touches on the ethical considerations and the need for responsible AI implementation.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week we look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations and what we can learn from it.Do you have a question for the expert? Ask it here using this Google form: https://forms.gle/8vzFNnPa94awARHMAAbout this week's guest: https://www.hpe.com/uk/en/leadership-bios/fidelma-russo.htmlSources and statistics cited in this episode:- AI industry growth: Statista - https://www.statista.com/outlook/tmo/artificial-intelligence/worldwide#:~:text=Artificial%20intelligence%20(AI)%20worldwide%20%2D%20statistics%20%26%20facts&text=The%20market%20for%20AI%20technologies%20is%20vast%2C%20amounting%20to%20around,trillion%20U.S.%20dollars%20by%202030 - Connectivity and networking industry size: Markets and Markets - https://www.marketsandmarkets.com/Market-Reports/wireless-connectivity-market-192605963.html - Security industry size: Grandview Research - https://www.grandviewresearch.com/industry-analysis/cyber-security-market - Storage industry size: Fortune Business Insights - https://www.fortunebusinessinsights.com/data-storage-market-102991- Green tech and Sustainability industry size: Fortune Business Insights - https://www.fortunebusinessinsights.com/green-technology-and-sustainability-market-102221Smartphone ‘X Ray' chip - https://ieeexplore.ieee.org/abstract/document/10381731

For our fourth episode of Working Smarter we're talking to Babak Hodjat, the chief technology officer for AI research at Cognizant. If you've ever used Apple's smart assistant Siri, then you're almost certainly familiar with his work.Hodjat has been doing AI research for nearly four decades, and if there's one thread that runs through his career, it's how to make working with digital agents feel as natural or effortless as working with a colleague. At Cognizant—an IT services and consulting firm—Hodjat's team puts this work into practice by helping other companies integrate AI tools into their workflows.Hear Hodjat discuss why it's still so hard for machines to understand exactly what we want them to do, the problems he's helping his customers solve, and how the latest generation of workplace assistants can help us make better decisions and improve the way we do our jobs.Show notes:Learn more about Cognizant's Advanced AI LabLinks to Hodjat's researchThe Perceptive Assistant that Learns (PAL)The Cognitive Assistant that Learns and Organizes (CALO)~ ~ ~Working Smarter is a new podcast from Dropbox about how AI is changing the way we work and get stuff done.You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube Music, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.aiThis show would not be possible without the talented team at Cosmic Standard, namely: our producers Samiah Adams and Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to Benjy Baptiste for production assistance, our marketing and PR consultant Meggan Ellingboe, and our illustrators, Fanny Luor and Justin Tran. Our theme song was created by Doug Stuart. Working Smarter is hosted by Matthew Braga.Thanks for listening!