Productive AI Podcast

In this episode, I speak with Daniel Jeffries. Daniel is a science-fiction author, engineer, futurist, thinker, blogger, systems architect, speaker, crypto nerd, AI evangelist, world traveler, beard-master, and overall renaissance man.Today we talk about a variety of topics including: why billionaires going to space is good; why how to make better predictions; how COVID will have long-term positive consequences for society; where we are in the long arc of AI; how the model development lifecycle supports and does not replace the software development lifecycle; where we are in terms of understanding MLOps; choosing between end-to-end and best-of-breed ML tools and platforms; what the AI Infrastructure Alliance is and how it's helping shape the future of ML Platforms; and what to think about when deploying AI/ML in your organization. It's a long and great conversation. Enjoy the ride!Timing00:00 Introduction02:15 Why billionaires going to space is a good thing04:13 Dan's thoughts on the Foundation series05:55 Predictions - good and bad - that you've made10:19 Thoughts on Kai-Fu Lee's “2041”12:40 COVID's long-term impacts on our society21:06 Where are we now in the arc of AI?27:12 This is still the early adopter phase29:42 Is AI really eating all software?31:36 The model development lifecycle vs. the software development lifecycle33:07 MLOps is still evolving as a term and as a practice36:07 MLOps is not just DevOps brought forward39:15 ML Platforms: End to end or best of breed components? (Or a blend?)40:34 The only end to end solution that exists is in the minds of marketers44:38 There is no LAMP stack for machine learning...yet47:54 What is the AI Infrastructure Alliance57:13 Blueprints and design patterns - making sense of the ML platform and tools space1:05:35 Platform rationalization and maturation is coming but it's not here yet1:07:30 How does a customer buy from members of the AIIA?1:11:45 Education is critical to long-term success1:17:15 As always, finding the right tool for the job is important1:21:45 There are two kinds of machine learning: basic and revolutionary1:24:35 Wrap-upLinksGet in touch with Dan:LinkedIn https://www.linkedin.com/in/danjeffries/Medium https://medium.com/@dan.jeffriesTwitter https://twitter.com/Dan_Jeffries1Amazon Author Page: https://www.amazon.com/Daniel-Jeffries/e/B00D1HG62U%3Fref=dbs_a_mng_rwt_scns_sharePatreon Page: https://www.patreon.com/danjeffries

In this episode, I talk with Will Uppington, CEO of TruEra about how trust is a critical output of machine learning and AI systems and AI quality management must be baked into development in order to build trust-worthy systems. We delved into the five core dimensions of model quality, the importance of iterating data and models in parallel, the state of the model development platforms and tools market, the regulatory environment around trusted AI, and ended with some career advice for people entering the field. -- Links --https://www.truera.comhttps://twimlcon.comAndrew Ng - From Model-centric to Data-centric AI:  https://www.youtube.com/watch?v=06-AZXmwHjo 

In this discussion with Colin Toal, CTO of Chisel.ai, we uncover the challenges being faced by this 5000 year old industry. We walk through the structure and practices of the commercial insurance sector, discuss why it works the way it does, and how AI is now helping to transform it one step at a time. We unpack the differences between basic insurance which is standardized vs. commercial insurance where every policy is a unique snowflake and how that poses challenges for automation and interconnection. We dig into the historical value of automation, and then we touch on the never-ending debate of build vs. buy. Finally, we closed the conversation with some advice to aspiring AI and machine learning engineers looking to build a career.-- Timing –00:00 Introduction01:03 Disruption in the insurance industry06:22 How AI is modernizing a 5000 year old industry11:09 When non-standardization and flexibility are features and not bugs13:27 Automation is an effort-reduction mechanism16:55 Colin’s winding road to Chisel19:15 Colin’s time at Amazon learning how to do machine learning at scale22:11 Bringing it all together - using technology to crack a hard industry problem28:03 Reduce your customer’s effort now and you’ll get to learn more about their business and help them even more in the future29:30 Why hasn’t this particular industry problem been solved with EDI or web services?37:04 Machines trained by humans are basically as good as humans on their best day...but every single day and 24x739:35 Once you’ve reduce the effort, you’ve also built a channel and you have more data than anybody else and you can expand your business from there45:56 The tech stack: Build, buy, borrow, or open source?53:08 Colin’s advice to aspiring AI and machine learning engineers -- Links -- https://www.chisel.aihttps://www.chisel.ai/work-at-chiselhttps://www.linkedin.com/company/chiselai/https://twitter.com/chiselai

Hear how the world’s best companies use Turing.com to hire the world’s best engineers. Listen to Vijay Krishnan, CTO and Co-Founder of Turing discuss how they match the best companies with the best global engineering talent using a combination of deep domain expertise and machine learning. Vijay also shares his lessons to founders and entrepreneurs as well as to aspiring machine learning and AI engineers.-- TIMING – 00:00 Introduction01:31 Vijay’s Machine Learning career05:16 Moving into an Executive in Residence (EIR) role to find the next big idea11:24 Advice to founders – Three important lessons15:12 The world was moving to remotely distributed teams before COVID17:04 If you know your idea is right, ignore the doubters (including the VCs)19:18 You have to be 10x better than anybody else19:54 Finding and narrowing in on the biggest, best idea23:50 What does Turing do and what is the value prop for customers and engineers?27:36 Never interview for another job again. Oh and work from your beach-house for Silicon valley companies.28:59 Why is it so hard for companies to hire top quality remote engineers without a platform like Turing?36:14 Team building is always hard. How do you make these remote teams work for the engineers and the people who hire them?38:14 Process and practices help build functional remote teams43:58 Why Turing insists on having good English skills as a baseline (spoiler alert: real-time machine language translation is not here yet.)46:07 What geographies does Turing serve in terms of customers?47:02 And what about the engineers and software developers?49:19 Why focus only on engineers? Why not any of the other surrounding roles?52:15 Stay hyper-focused so that you can be 20x-30x better than the competition52:58 Does Turing also work with machine learning and data science engineers?54:12 What is Turing’s business model?58:07 Where does Turing USE machine learning?01:04:00 Raising another round of financing01:06:30 What is Turing hiring for these days in terms of roles?01:07:21 Senior engineering talent is in really short supply01:09:35 Advice to founders: Nail the market – make sure it’s big01:12:09 Advice to engineers: Learn the foundations but also learn the business01:17:00 Wrapping up!01:18:47 Signing off -- LINKS -- Website: http://turing.com Hire Top Remote Developers: http://turing.com/hire-engineers Apply to Top US and Silicon Valley Remote Developer Jobs: http://turing.com/jobs

How can an AI understand language? Computer-human communication is undergoing a revolution and AI can now listen to, understand, and speak back to us in much more powerful ways than it could before. On this episode, hear Scott Leishman discuss how AI can now write news articles, blog posts, poetry, and novels and how work done in the recent past is making it easier than ever to build incredibly powerful AI applications that can communicate with human beings. -- TIMING – 00:00 Introduction00:48 Scott’s background in computer science at FICO, Core Logic, and Nirvana Systems (which exited to Intel for $400M in 2016), and Intel06:56 What is Natural Language Processing (NLP)?11:40 What was the significance of GPT-3’s release this year?16:31 What can GPT-3 do? (explain it to somebody who doesn’t follow the field). 19:15 NLP is having its “ImageNet moment” – what does that mean? (Technical explanation)25:39 Simplifying NLP for less-technical listeners28:17 Standing on the shoulders of giants: Pre-trained models are making it easier to build AI applications30:05 What kinds of new uses cases are possible with the current state of the art NLP?33:29 Apple Knowledge Navigator – are we there yet?37:25 Where does NLP live in the AI stack?41:34 What are you doing with NLP at XOKind?49:47 What should people be doing to improve their chances of working in this space?54:05 Summary  -- LINKS -- Books: Manning & Jurafsky is sort of the best known, comprehensive but is a bit dated at this point. Fortunately they are working on a new draft: https://web.stanford.edu/~jurafsky/slp3/ Conferences: the big ones for NLP are ACL, EMNLP (was just last week), CoNLL, but you’ll also see a lot of new work at ICLR and NeurIPS Papers. The field moves quick but arXiv is the first place to find new results. I’d highly recommend searching through something like arxiv-sanity instead for a subject/topic of interest. Mailing lists: I’m a big fan of Sebastian Reuder’s monthly update, you can sign up for at NLP news https://ruder.io/nlp-news/ Sites: I mentioned https://nlpprogress.com/ to keep tabs on current state of the art for given downstream tasksFor folks that want a good practical introduction I’d recommend Stanford’s undergraduate NLP course (complete with video lectures online): http://web.stanford.edu/class/cs224n/ Getting interested in ML in general, this course is pretty good too if you have some programming experience under your belt: https://course.fast.ai/ Hugging Face are doing a lot of great work in the NLP space, they have easy integrations for various models, a solid python library etc. Rasa are another open source solution, they now have APIs too for helping build conversation agents XOKind! Sign up for our mailing list on the front page here: https://www.xokind.com/ Job openings. List is here: https://www.xokind.com/careers/ (scroll down the page). Growing Frontend and Backend engineering is a current focus for us. Apple Knowledge Navigator Video: https://www.youtube.com/watch?v=HGYFEI6uLy0

Listen in to my conversation with Kevin Tu from DFJ Growth while we discuss the next ten years of the AI market, the difference between AI-enabled companies and AI-first companies, characteristics of well-funded startups, the structure of the AI ecosystem, whether AI businesses are really different from a business model perspective, when to build vs. buy AI infrastructure, and finally, some advice to entrepreneurs building AI focused companies. -- Timing – 00:00 Introduction01:35 Kevin’s background from engineering to finance and then venture capital05:38 The next ten years of the A.I. market09:31 AI-enabled vs. AI-first companies14:23 What is DFJ Growth looking for and what are a couple of examples of recent funding? (Neocis and DataRobot)21:06 What are some characteristics of companies that will have a higher chance of success?23:23 Structure of the ecosystem – layers of the stack28:20 Are the business models of AI companies really that different?29:50 In the early days you do the unscalable work, and then optimize and scale later31:56 Should startups roll their own infrastructure or leverage existing cloud infrastructure? 35:05 Advice to builders and entrepreneurs38:21 When should a startup reach out to you or your team?40:28 Contact information41:09 Wrap-up -- Links -- https://www.dfjgrowth.comhttps://www.linkedin.com/in/kevinbenjamintu/https://twitter.com/kevbtu

It is now possible to radically grow your business by improving the hand-off between marketing, sales, customer success, and even finance through the use of advanced intelligent virtual assistants. In this podcast, we’ll talk to Jim Kaskade, CEO of Conversica about how they’re helping their 2000 global customers speed up communications, improve customer satisfaction, get better lead coverage, qualify prospects more effectively, and increase deal close rates (up to 400% improvement!) -- Timing – 00:00 Introduction01:21 Sales and marketing have been traditionally misaligned and out of sync03:00 Unpacking the marketing to sales process03:20 Marketing often has a low return on investment and a lot of waste04:19 Lead nurturing might need two follow-ups but probably more likely needs ten or maybe twenty to be successful05:14 Sales teams are drowning in in-bound “marketing qualified leads”07:15 There are over 8,000 marketing apps and many sales apps, what’s the gap here?09:05 Digital transformation and marketing automation has caused a volume problem that the human sales reps can’t handle10:46 Sales teams now have a filtering and prioritizing problem11:56 If you haven’t automated marketing, you don’t have these problems yet!13:55 Automating the back office is about cost optimization, optimizing the front office (sales, marketing, customer experience) is about increasing revenue14:20 Solving this overload and filtering problem with AI15:06 From Clippy the paperclip to Intelligent virtual assistants, it has been a long road17:07 Moving way beyond the initial IVA use cases of technical support and into marketing and sales18:30 You could have a conversation with an IVA for weeks or months until you’re ready to buy20:43 Deep learning has significantly changed the game 22:04 Applying and productizing AI to solve a problem…is harder than solving the AI at the core of the product23:46 Where do Amazon, Microsoft, and Google fit into the picture in terms of providing Natural Language Processing engines?25:07 How does a customer operationalize something like this? How do they install and use it?26:01 Platforms are too hard for many customers so we deliver this as an application27:30 We sell in a way that’s understandable and budgetable – by the “virtual assistant” – assigned to a departmental budget29:24 You have actual working inside marketing reps, SDRs, and inside sales agents?30:21 Are these IVAs replacing people? Or augmenting them?32:04 What if every human member of their team had an assistant who could help move business along?34:00 Can these assistants replace field sales people or people working high-touch, complex, multi-buyer deals?35:29 What it’s like when you have virtual team members and how they can hand-off customers to other virtual assistants or to humans39:30 This all sounds like science fiction41:00 Use cases – educational course selection, technology conference attendance42:50 The value of true intelligent virtual assistants – increased close rates (up to 4x improvement); reconnecting with customers who are dropping off usage (CX); 176x pipeline growth; 10x revenue; higher customer retention rates; faster cashflow from receivables and more45:35 On the internet nobody knows you’re an intelligent virtual assistant – the ethics of disclosure49:18 98% of the time, people think they’re talking to a real human50:03 The present and future markets of intelligent automation, robotic process automation, chatbots, conversational AI, and intelligent virtual assistants53:58 Where is Conversica focused?57:34 Wrap up and contact info59:01 Sign-off -- Links --https://conversica.com

It is possible to use AI based speech in many business situations. We’re entering a new era where computers are so good at communicating with us via both spoken word and text conversations that they can perform tasks traditionally handled by call centers. In this conversation with the CEO of Inference Solutions, you’ll learn about the long arc of Natural Language Processing, Conversational AI, and the rise of the Intelligent Virtual Agent Market. We also discuss AI product management and positioning and pricing that will be helpful for anybody building a complex AI-based product or service. Finally we close out with some advice to buyers who are trying to make sense of a noisy marketplace. NOTE: Since the recording of this podcast, Inference was acquired by Five9 (www.five9.com).Timing:00:00 Introduction01:03 Callan’s career background02:44 How Inference is leading their segment (and being recognized for it)03:45 Intelligent Virtual agents vs. business process outsourcing (BPO)05:16 The importance of channel partners to access the market06:22 How does the platform work?07:50 Where did the Conversational AI industry come from?12:11 What is the current state of the technology?14:38 Interesting uses for your technology18:10 The development of technology and maturity curves in NLP, text to speech, speech recognition, etc.19:06 NLP engines got commoditized20:34 AI Product Management 101 – don’t get lost in the AI tech, focus on the overall business problem and solve for that22:39 Summary of the business model and offerings23:14 The Intelligent Virtual Agent market category has become a real market segment24:51 How many channels can your system communicate on? (Voice, text, web-chat, message services, WhatsApp, etc.)27:36 How are you different from other players in this market segment?31:18 Pricing innovation is still innovation – pricing simplicity helps drive business35:34 Where can you best apply this kind of technology – to achieve what objectives and to do what jobs?36:58 What will the next few years bring?39:30 This market is noisy – there are 2000+ “chatbot companies” – but if you need something multi-channel (voice + text), it drops to 2042:46 Callan’s advice to buyers in the market44:53 Connecting with Callan and the team at Inference46:07 Wrap-up! Links:https://www.inferencesolutions.comcallan.schebella@inferencesolutions.com 

Marketers today are drowning in data and have little insight. Too many dashboards from too many channels, all with different user interfaces and data schemas, means that making sense of it all takes too much time, and often doesn’t lead to clear insights such as “this channel doesn’t work and we should stop spending on it”. To make matters worse, each platform’s analytics are siloed and their goal is to increase, not decrease, your spend on that platform. GlanceHQ.ai was formed by a team of marketing agency experts to solve this problem Hear Roy Nallapeta discuss the state of the industry, why marketing software needs to be more like a Tesla, and how leveraging artificial intelligence and machine learning can help marketers make better and more effective allocation decisions in much less time.To see or hear more episodes:Sign up on our site at https://productiveai.com/signup/ to be notified of future episodes.Subscribe on Youtube.Subscribe to the Productive AI podcast at Apple, Google, Spotify, ListenNotes, or Radio.com.Timing:00:00 Introduction01:29 Roy’s background and career02:38 About GlanceHQ.ai03:30 The Marketing Automation and software market04:42 The Big AHA moment – every marketer has the same questions, too many dashboards, and no answers06:30 Making channel allocation investment decisions is brutal for marketers08:39 If your car can drive across town, your marketing app should be able to run itself too09:15 How does all this magic work?13:21 What’s it like to live with this kind of toolset compared to how it’s done today?18:01 What if your intelligent marketing co-pilot could identify risk and predict campaign success?20:06 What if a marketer could save 30% of their time and avoid cost misallocation of funds to the wrong channels? 25:55 Who’s the competition? Doing nothing and using too many dashboards27:25 What’s your business model for Glance and who are your customers?30:28 Connecting with the GlanceHQ.ai team31:25 Closing commentsLinks:https://glancehq.ai https://www.linkedin.com/in/rohitnallapeta/ https://medium.com/@katyasakovich/startups-for-digital-marketing-from-disrupt-sf18-expo-94beee15cf02 

Self-driving cars must get better at understanding people’s intentions by “reading” the body language of the humans around them, so that they can co-exist more safely with us. In this amazing discussion with the Co-founder of Perceptive Automata, we will learn how a branch of cognitive science known as psychophysics is being used to teach cars about the intentions of the humans around them so that they can be better and safer drivers. To see or hear more episodes:Sign up on our site at https://productiveai.com/signup/ to be notified of future episodes.Subscribe on Youtube.Subscribe to the Productive AI podcast at Apple, Google, Spotify, ListenNotes, or Radio.com. -- TIMING --00:00 Introduction01:12 Sam’s career05:04 Perceptive Au-TAW-mah-ta, not Au-to-MAH-ta05:52 How are you teaching cars to understand human intention?11:25 Structure of the autonomous vehicle market and the 5 Levels of autonomous driving17:06 The Autonomous Vehicle technology stack23:25 Use case discussion – how does an intuitive car understand multiple scenarios?31:25 Cars will have general purpose compute platforms33:49 Where does Tesla fit in here?38:14 Typical customers for Perceptive Automata’s tools41:09 Can intuition extend to other non-vehicle environments such as hospitality, delivery robots, and construction42:34 What about the military applications?46:52 Summary of market and global need48:44 How much of this is Edge AI vs. being processed in the datacenter or cloud?49:53 What does it take to train AI to understand human body language?53:00 Career advice for people interested in getting into the autonomous vehicle market57:48 How to contact Perceptive Automata and Sam59:14 Close -- LINKS --https://www.perceptiveautomata.com https://twitter.com/sam_e_anthony https://www.linkedin.com/in/sam-anthony-19a65917/If you found this podcast episode helpful, don’t forget to subscribe at https://productiveai.com/signup/DISCLOSURE: To support the channel, we use referral links wherever possible, which means if you click one of the links in this video or description and make a purchase, we may receive a small commission or other compensation. 

Neural networks can be made faster, cheaper, and smaller. This can result in higher performing and lower cost operation of complex AI applications at the edge of the network –where ever that edge might be such as a factory, vehicle, ship, or other remote location. In this episode, hear Jags Kandasamy explain how Latent AI’s development platform helps customers and suppliers in every industry compress and adapt neural networks to run “at the edge” and how this ultimately speeds up application development and delivery, as well as improves the performance of the AI application itself. Also we’ll touch on the relationship between Edge AI, and 5G.Subscribe to get notified of future blog posts and podcast episodes: https://productiveai.com/signup/ -- TIMING --00:00 Introduction00:47 Genesis story03:30 What is Edge computing? And Edge AI?07:02 Wearables and smart watches as edge devices08:04 Video cameras as edge devices11:38 Edge AI can assist with maintaining privacy12:43 Deep learning is too compute heavy for the edge15:07 Automotive production example: predictive maintenance20:59 LEIP Compress compresses the model to 1/10th the size while only reducing predictive accuracy a few percent24:08 LEIP Compile targets various end hardware devices so that the developers don’t have to keep track of it all27:40 AI accelerator chips and hardware are exploding29:49 The telco use case: AI at the edge of the telco network and Content delivery network34:04 Where to focus when there are so many opportunities in so many sectors?38:19 Partners and system integrators are required in order to scale40:26 What types of customers are a fit for Latent AI?43:40 Wrap-up! -- LINKS --https://latentai.comhttps://www.linkedin.com/in/jagsk/https://en.wikipedia.org/wiki/Edge_computing If you found this podcast episode helpful, don’t forget to subscribe at https://productiveai.com/signup/ DISCLOSURE: To support the channel, we use referral links wherever possible, which means if you click one of the links in this video or description and make a purchase, we may receive a small commission or other compensation. 

Hear Seth Clark explain how the largest Military, Civil government, and Enterprises design, build, and deploy large-scale AI projects from the lab into production in a way that is quick, safe, and secure. Topics include: the AI Pipeline; the differences between model management, Model Ops, and MLOps; what all the members of an AI team should do (and more importantly, NOT do); how the answer is not build-or-buy, but build-AND-buy; what new threats exist in an AI application and how to defend against them; as well as how to build AI systems that can explain their decisions to humans.Timing:00:00 Introduction00:49 The genesis story of Modzy – born from Booz Allen consulting engagements03:24 Working with Military, civil government, finance, oil & gas, energy and utilities05:28 Why do customers need a platform? Why not just DIY?08:30 Description of Modzy – the platform and model marketplace15:00 The AI pipeline – from big idea to data collection, model development, training, deployment, assessment, retraining, explainability21:00 What is the difference between model management, Model Ops, and MLOps?22:42 Deploy to where? Tactical edge, public cloud, private datacenter, air-gapped datacenter25:30 AI is a team sport – data scientists, software developers, machine learning engineers, business analysts, executives30:48 Model Marketplace – an app store for AI models33:42 AI requires adversarial defense mechanisms to protect against new and different attacks37:03 What is explainable AI, why do we need it, and how do you achieve it?41:45 Ideal customer for a platform like this44:49 Wrap-upLinks:https://www.modzy.com https://www.linkedin.com/in/seth-clark-0820b0b/

Hear Bharath Gaddam explain how your company can save or reallocate 20% of its marketing spend by establishing a clear cause between online marketing, offline marketing, and current and future sales using the power of AI, in particular, deep learning. He also digs into how 98% of the marketing automation industry is failing its customers by using the wrong tools for the job and what his team is doing about it. Timing:00:00 Introduction00:46 Bharath’s career – the genesis of DataPOEM05:32 The DataPOEM hypothesis – connected intelligent decision support for Marketers06:55 The Martech 5000 (which is really 8000+)09:04 The language and semantics of “Marketing ROI” is all wrong – how marketing decision makers are being misled by the vendors12:05 Market Mix Modelling, Multi-touch attribution, and hybrid solutions and what they’re missing15:13 Our mission – No FUQs (Frequently UNanswered questions!)17:43 All existing systems only solve operational issues, not leaders issues20:00 Marketing leaders don’t have the information they need so they fall back to Excel and mental models.22:54 Current vendors are using the wrong tool for the job – they’re attempting (and failing) to solve complex multi-variate problems with simple single-variable tools26:55 The solution big idea – a holistic approach to solving the problems30:28 Using syndicated data sources to rely less on the customer bringing the data31:00 Integrations with 200+ data sources34:34 How deep learning solved our problem 42:19 Summary of the product44:32 Who are the best fit customers for a solution like this?Links:https://datapoem.com/https://www.linkedin.com/in/bharath-gaddam-3355a821/https://chiefmartec.com/2020/04/marketing-technology-landscape-2020-martech-5000/

Description: Hear Mark Cramer explain his view on Product Management, his definitions of AI and related fields, why he thinks AI is cool and life-long learning is important, how the job of the AI Product manager is challenging (and more fun!) than a regular Product Management job, and what to do if you’re considering becoming an AI product manager. Timing:00:00 Introduction01:28 Mark’s career04:57 Why is AI Product management more than just product management?05:25 What is Product Management (without AI)?14:52 What is AI? (And Machine learning and deep learning)26:44 AI Product management requires more than just product management skills40:58 The always critical MVP (Minimum Viable Product) applied to AI products52:07 Resources for people wanting to be an AI product manager54:00 Wrap-up!Links:Mark Cramer: https://www.linkedin.com/in/mcramer/Mark’s Writing:Magic Dust for Artificial Intelligence Product Managers: https://www.linkedin.com/pulse/magic-dust-artificial-intelligence-product-managers-mark-cramer/Learnin’ Good All this AI Stuff for Product Management: https://www.linkedin.com/pulse/learin-good-all-ai-stuff-product-management-mark-cramer/Communicating a Red-Hot AI Value Proposition to Your Stakeholders: https://www.linkedin.com/pulse/communicating-red-hot-ai-value-proposition-your-mark-cramer/AI PM WFH SIP TBR, IMHO; YOLO: https://medium.com/@markdcramer/ai-pm-wfh-tbr-imho-yolo-ddb1497716bOTHER PEOPLEEric Ries / The Lean Startup: http://theleanstartup.comSteve Blank: https://steveblank.comRandy Komisar: https://www.kleinerperkins.com/people/randy-komisar/ OTHER RELATED BOOKS:The Four Steps to the Epiphany by Steve Blank: https://amzn.to/36e8rkZThe Startup Owner’s Manual: https://amzn.to/3cDHLeJThe Lean Startup by Eric Ries: https://amzn.to/3cIGB1tThe Startup Way by Eric Ries. https://amzn.to/2S6JM9RThe Monk and the Riddle by Randy Komisar: https://amzn.to/2S7YEVCGetting to Plan B by John Mullins and Randy Komisar: https://amzn.to/2Hzk1NfAffiliate Links used where possible!DISCLOSURE: We often review or link to products & services we regularly use or have tested or evaluated and think you might find helpful. To support the channel, we use referral links wherever possible, which means if you click one of the links in this video or description and make a purchase we may receive a small commission or other compensation. We're big fans of Amazon, and many of our links to products/gear are links to those products on Amazon. We are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and related sites. 

Timing:00:00 Welcome01:02 Definitions of AI, Expert systems, Machine Learning, Deep Learning, and Neural Networks05:00 Generative Adversarial Networks and the need for more data07:26 Strong vs. Weak AI09:52 Artificial General Intelligence: ignore it or immiment threat?11:41 The next 10 years of AI15:15 Adoption of AI in the enterprise22:38 On the difficulty of building an AI product company and how it impacts your margins25:17 What is the state of the art in AI?27:55 Open AI’s GPT-330:55 The impact of AI on the work force – will it take our jobs?32:23 Robotic Process Automation (RPA) is already taking jobs34:27 Kai-Fu Lee’s grid on the impact of AI on the work force37:32 What is the relationship between AI and 5G, edge computing, and quantum computing?44:44 Advice to enterprises who want to adopt AI48:43 Contact information for TomLinksTom Taulli: http://www.tomtaulli.comArtificial Intelligence Basics: A Non-Technical Introduction (Kindle/Amazon): https://amzn.to/2ZVapD6The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems:https://amzn.to/2FS4tU5Tom Taulli on Forbes: https://www.forbes.com/sites/tomtaulli/Twitter: https://twitter.com/ttaulliOther books mentioned:AI Superpowers: China, Silicon Valley, and the New World Order by Dr. Kai-Fu Lee: https://amzn.to/3hNePljAffiliate Links used where possible!DISCLOSURE: We often review or link to products & services we regularly use or have tested or evaluated and think you might find helpful. To support the channel, we use referral links wherever possible, which means if you click one of the links in this video or description and make a purchase we may receive a small commission or other compensation. We're big fans of Amazon, and many of our links to products/gear are links to those products on Amazon. We are a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for us to earn fees by linking to Amazon.com and related sites.

In this episode, we introduce the Productive AI podcast. In this podcast, we will focus on three distinct audiences and conversations: We will speak with product teams selling AI-related products and services about why they're building their product, who it's for, what problems it solves, and value it delivers, and what they're ultimately achieving for their customers. We will speak to buyers, whether they are in the C-suite or the field, to help them better understand how this market is developing, so that they know what's possible, now vs. later. We'll also discuss strategy, vendor selection, and enterprise integration. We will also speak with project teams who are in the trenches, executing AI projects to uncover lessons learned and obstacles to success and potential ways to overcome those obstacles. The goal is to share the lessons learned so that others may follow in their footsteps and have a smoother path.