Join us on this fascinating journey as we sit down with Jann Tallinn, the founder of Skype and Kazaa, to explore the groundbreaking world of AI and its potential implications for society and businesses. 

Listen in as we tackle the difference between summoning AI and aliens and discuss how it impacts our ability to control the outcomes of AI development. We also delve into the idea of computational universality, the Church-Turing thesis, and how AI is advancing rapidly due to the need for significant computational resources. Additionally, we ponder if aligning AI development more closely with the functioning of our brain could lead to a decrease in the computational power currently required for AI. 

The conversation doesn't stop there, though. We venture into an examination of the three components that can potentially increase AI power and how context learning allows a model to modify its behavior according to a given context. The risks associated with AI's black box nature, the difficulty of predicting how AI might act in the future, the public's attitude towards AI, its potential economic implications, and the increasing leverage of technology are all on the table. Jaan shares his insights on these critical topics as we underscore the fact that the number of futures that contain humans is a small target as technology advances. 

Lastly, we discuss the potential implications of AI and how it differs from the human brain. Jaan provides intriguing insights into the need for regulation and the potential pitfalls of having one company control the compute. We debate the pros and cons of constraining AI experiments and consider the potential risks of centralization versus existential risks. Don't miss out on this illuminating conversation with Jaan Tallinn as we traverse the captivating world of AI.

Key Quotes: 

• Ultimately, we were developed by evolution, right? So it's like an evolution had like no idea what it was doing. It wasn't planning ahead like at all. Ultimately there is no kind of like knockdown argument that you can't reach things with just throwing compute that it. It's clear that human brains are doing things that no AI are currently doing.
• I just like file it under the general problem that are the current paradigm of neural networks just are black boxes that are grown, not built. therefore they're very hard to get actual guarantees about their performance and the performance failures, again as you're gonna repeat the value of AI is kind of mostly measured by its mistakes or by, its more generally by its misbehavior.
• We have had warnings from the people like Alan Turing from 70 years ago. He said that once AI becomes as powerful as humans we should expect to lose control to it. We didn't really spend much of that 70 years researching ways how to remain in control or how to make sure that the future goes well with AI that is potentially more powerful than humans. So we should catch up. We should to do more, more [of] that research. But in order to do that research, we need time.
•  

Time Stamps: 

(00:24) -  AI's Risks and Opportunities

(13:34) -  AI Advancements, Risks, and Regulation

(20:13) -  Neural Networks and the Need for Regulation

(25:18) -  Centralization Risks Versus Existential Risks

Links:

Learn more about Jaan Tallinn

Learn more about the Future of Life Institute 

Connect with Vahe

Check out all things Cognaize

In our insightful conversation with Alex Singla, Senior Partner at McKinsey & Company and Global Leader of QuantumBlack, AI by McKinsey, we tackle the most commonly asked questions about generative AI and how to implement it effectively. 

Listen in as Alex sheds light on identifying company-specific opportunities, organizing and governing AI tools, balancing risk and value creation, and navigating the future of talent and tech stacks. We also take a look at the journey of getting started and learning quickly to harness the power of generative AI. We explore the art of creating cost-efficient, scalable solutions that drive adoption and navigating the learning process to maximize speed, cost structure, and reusable code. 

Our conversation focuses on the delicate balance between moving too fast and too slow in the competitive AI space. We also weigh in on the role of large language models in achieving industry-specific solutions and how to optimize them for efficiency. Further into our discussion, we address the complexities of building and leveraging large language models, focusing on the costs, pros and cons of in-house building, and the importance of data privacy and IP protection. We then examine the skills needed to run these models, the learning curve, and the economic value derived from this process. 

The discussion concludes with a look at how generative AI can be used to improve customer experience and how to implement safeguards to avoid unethical behavior. Join us for this informative and thought-provoking episode with Alex Singla!

Key Quotes: 

• In many worlds, speed itself is a strategy. If you and I were doing exactly the same thing, but I'm just faster, every time I'm faster, I will continuously beat you, right? And so, speed itself is a strategy. What I don't mean by that is like reckless speed, I'm not saying that. I'm saying in general, speed is a strategy.
• Some of what you learn is not just what you build, like what solution you build, but how you build it matters a lot. Why the how matters is because if you build things in a way that get to the better answer faster, you ping off the cloud less often, and therefore you have less consumption. If you provide better results on your first query versus your fifth query, your employees or your customers get a better response faster. And so all these things make, have impact on scalability and cost. 
• You don't want to be the last person in the race when your industry is taking off.Now, at the same time,in my conversations with clients, it's actually funny.I get framed two things with CEOs often, which is, I don't want to be the fastest person out there who blows up my organization because I made a bunch of mistakes. And I don't want to be the last guy who was waiting so long that everyone overtook me and I became irrelevant.
• Those who customize, add some things on top and make it more specific, and leverage their own proprietary data and insights, I think that's how you win.I think that's how you create differentiation and strategic distance.
• How you build matters a lot. There isn't a company that I don't talk to that worries about IP, data, privacy. It's actually quite inspiring to hear every, every CEO, every top team thinking about IP and data privacy and protecting their customers' data. And basically not making sure their information doesn't just make the LLM better, which then makes everybody better. And so the how, I think, is a structured way in terms of, you know, do you bring those models in-house within your four walls and then only run your proprietary information on those, on those models? That's what I see most companies are doing, right? They're leveraging the large language, the external large language models for things that are completely not competitive. 
• As people build these tools, it's so critical they build them into the day-to-day workflow of how someone acts, right, and someone behaves.

Time stamps:

• (01:09) - The biggest questions customers have when it comes to AI
• (06:53) - How speed has become a business strategy
• (11:36) - Converging on large LLMs
• (16:02) - How companies are leveraging large, public LLMs, to protect customers
• (22:29) - Automation and Human Involvement in AI

Links:

Connect with Alex

Visit McKinsey & Company

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Visit Cognaize

Join us for an engaging conversation with Philipp Wehn, Vice President of Innovation & Venture Building at Siemens, as we dive deep into the exciting realm where artificial intelligence and real-time location systems intersect.

In this episode, Philipp unveils Siemens' remarkable journey of harnessing AI to achieve unparalleled accuracy in Real-Time Location Systems (RTLS). He shares inspiring real-life stories, including a deeply personal account of how this cutting-edge technology played a pivotal role in ensuring the safety of his newborn child in a hospital. Discover the myriad applications of AI-powered RTLS in healthcare and manufacturing, and how it's revolutionizing these industries.

We also embark on a journey through the evolution of smart buildings. Philipp compares traditional homes with their smart counterparts, examining the transformative influence of voice models like Siri and Alexa in reshaping our interactions with living spaces. Dive into the world of computer vision, with a special focus on its rapid growth in China, and explore the pressing challenges of safeguarding data privacy in the future of smart buildings.

Philipp sheds light on the integration of AI into corporate strategies, discussing its profound impact on productivity. Together, we contemplate the concept of autonomous enterprises and the potential for AI to liberate humans from mundane tasks, empowering us to tackle more complex and impactful challenges. This riveting conversation is filled with intriguing insights and real-world examples of AI in action that you won't want to miss.

Key Quotes: 

• The first thing is we can tell a hospital the exact utilization rate of an asset or an asset class. And then secondly, we can make sure that the assets that are not used are put away. And then we can do also asset loss prevention through virtual geofencing.So we can make sure if an asset leaves a certain area or enters a certain area, like the exit zone, we can send an alert to nurses or to staff. So that 20 percent asset loss can be reduced to approximately 5 percent through real time location services. And that is a huge moneymaker for healthcare.
• Through GenAI, we will see an acceleration of these voice models.Siri, Alexa, and others. And then the second thing, geofencing, as soon as I leave the building, the house, um, my AC and heating goes to an echo mode, and then I can also opt into like a smart  AI driven schedule that is pretty much based on all other houses in the area, and that adapts my schedule to maximize comfort and efficiency.
• I think in our Western society, the one thing that keeps us away from major progress through the results from computer vision is aata privacy. Many people, they really care about their data privacy and I get that. I am the opposite. I always sa I'm a glass person. Take my data as soon as, as long as you make my life better, take it.
• I transformed completely within the first year to be an AI first person. With AI first, I mean, whenever I do anything. I asked myself, can AI help me? And I have different applications for that. This is also part of the Gen AI strategy that we put together here in the company but for different fields like writing collaterals, summarizing meetings, putting together decks,  preparing for a session, summarizing a competitive report.I would say Generative AI in the past six months, 5x’d my personal productivity in my job. Just because whenever I do something, I ask myself, which tool can I use to accelerate the process to a result? And let's be honest, the result isn't perfect immediately, but I get from 0 to 80 percent within 30 seconds. And that is the time that, that before generative AI, that drained my head, that made me tired, getting from 0 to 80. But now I get this 80 percent perfect piece of something, and I can use my human brain to make it awesome.
• I think that the holy grail will be understanding the model. Pre filtering data that flows into it, fine tuning it in the end, and then creating a solution that is trustworthy and that solves a problem that wasn't possible to be solved before the rise of Gen AI
• I have the vision of of autonomous enterprises a couple decades in the future where we, we maybe have a society where we don't have competition between enterprises anymore and the enterprises interact with each other and just generate products and wealth for us so that we can return to do what humans can do best, be creative, invent things, save this planet and get, get all the knowledge workers out of these jobs where they do repetitive things, like they would be in an automotive car factory and do the same thing every single day. These people are smart, these people are creative, and I think AI has the potential to free them up and do what we as humans can do best. Which is solve problems, be creative, invent the future.

Time stamps:

(00:43) RTLS accuracy with AI

(3:45) How hospitals utilize RTLS for increased security 

(11:30) Expanding to smart homes and centers

(14:45) Expanding machine learning through the use of cameras 

(18:50) Venture building and energy efficiency 

(23:08) Protecting data privacy 

(28:27) Building Trust and Sustainability of AI

Welcome to "Hybrid Minds: Unlocking The Power of AI + IQ," the podcast that explores the groundbreaking concept of combining artificial intelligence with human expertise to achieve unparalleled advancements in various fields. 

In this era of rapid technological evolution, we delve into the extraordinary potential AND conflict that arises when AI and human intelligence merge, ultimately giving birth to the concept of something we call, “hybrid intelligence”.

Join us as we explore the remarkable intersection between AI and human intelligence and dive into the captivating world where AI demonstrates its prowess in surpassing human capabilities. And delve into the controversy of the real dangers and pitfalls of AI.

In each episode, we'll bring you fascinating discussions with experts at the forefront of this hybrid intelligence revolution. Discover the transformative impact this collaboration has on various industries, from finance and engineering to education and beyond.

Join us for "Hybrid Minds: The Power of AI + IQ", powered by the minds at Cognaize.