what are the things happening Gen AI - MASTERCLASS in മലയാളം | The Possibility Project Podcast | Episode 2 PART 1 | Gen AI The Rise of AI: A Journey Through Data and Technological Advancements This blog post summarizes the evolution of Artificial Intelligence (AI), focusing on the factors that fueled its recent explosive growth. We'll explore the historical context, the role of data and computing power, and the impact of generative AI models. Early Days and the Turing Test Early conceptions of AI, dating back to the 1960s, focused on creating machines with human-like intelligence. The Turing Test , proposed by Alan Turing, aimed to determine a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. This involved a human evaluator engaging in conversations with both a human and a machine, attempting to differentiate between them. The initial focus was on simple tests, but the goal remained to create AI that could convincingly mimic human conversation. The AI Winter and the Dawn of a New Era Despite early optimism, AI development faced a period of stagnation, often referred to as the "AI winter." This was partly due to limitations in computing power and data availability. Recent advancements, however, have led to a resurgence of interest and investment in AI. This is largely attributed to the confluence of several factors: Increased computing power: The advent of powerful GPUs significantly accelerated AI model training. Explosion of data: The rise of social media and the internet generated massive datasets crucial for training sophisticated AI models. Advancements in machine learning: Techniques like machine learning , robotic process automation (RPA) , big data analytics , and predictive analytics have become increasingly sophisticated. The Role of Data and Affordability The availability of affordable data played a crucial role in the AI boom. The widespread adoption of smartphones and affordable internet access, particularly in countries like India, led to an unprecedented surge in data generation. The cost of internet access decreased dramatically, making it accessible to a vast population. This fueled the creation of a massive amount of user-generated content, including videos and social media posts. Affordability timeline: Early 2000s: Limited internet access and high bandwidth costs. 2013 onwards: Significant increase in affordability, leading to a surge in data consumption. The Creator Economy and the Data Deluge The increased data affordability led to the rise of the creator economy . Millions of individuals started creating and sharing content online, contributing to an exponential increase in data. Between 2015 and 2020, 99% of the data ever produced by humanity was generated. This massive dataset provided the fuel for training advanced AI models. The shift from primarily text-based internet use to multimedia consumption (videos, images) significantly increased data volume. Generative AI and the Current Landscape The abundance of data, coupled with advancements in computing power, enabled the development of large language models (LLMs) . These models, like GPT, are trained on massive datasets of text and code, allowing them to generate human-quality text, translate languages, and answer questions. Generative AI distinguishes itself from traditional AI by its ability to produce new content rather than simply analyzing existing data. The rise of generative AI has led to new applications in various fields, including art generation (Midjourney, Stable Diffusion), text generation (ChatGPT), and code generation. The Impact of Generative AI Models Generative AI models have seen unprecedented adoption rates, with platforms like ChatGPT reaching millions of users within months. This surpasses the adoption rates of other popular platforms like Instagram and TikTok. The economic implications are significant, with new business models and opportunities emerging. The availability of powerful AI tools at relatively low costs is democratizing access to sophisticated technologies. However, challenges remain, including the cost of running these models (requiring powerful GPUs) and ethical considerations surrounding their use. Technological Leap and Future Outlook The advancements in GPU technology have been crucial for the development and deployment of generative AI models. The increased processing power allows for faster training and inference. The future of AI is likely to be shaped by ongoing advancements in hardware and software, leading to even more powerful and versatile AI systems. Key Takeaways: The rapid advancement of AI is a result of a confluence of factors, including increased computing power, the explosion of data driven by the creator economy and affordable internet access, and the development of sophisticated machine learning techniques. Generative AI models represent a significant leap forward, offering new possibilities across various industries, but also posing new challenges that need to be addressed. Gen AI - MASTERCLASS in മലയാളം | The Possibility Project Podcast | Episode 2 PART 1 | Gen AI The Rise of AI: A Journey Through Data, Technology, and Accessibility This blog post summarizes the key developments in the field of Artificial Intelligence (AI), focusing on its evolution, the role of data and computing power, and its impact on the creator economy. Early Days and the Turing Test The concept of AI, similar to human intelligence, emerged in the 1960s. The Turing Test , designed to assess a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human, was a significant milestone. The test involved a human evaluator engaging in natural language conversations with both a human and a machine, attempting to distinguish between them. The Maturity of AI and Contextual Understanding Recent advancements have led to AI systems that are becoming increasingly indistinguishable from humans in conversation. Contextual intelligence is a key differentiator, allowing AI to understand nuances and respond appropriately without requiring excessive questioning. This contrasts with earlier, simpler AI tests. The integration of machine learning , robotic process automation (RPA) , big data analytics , and predictive analytics has significantly contributed to AI's capabilities. The Role of Data and Computing Power The explosion of data in the last 10-15 years, fueled by the growth of social media and increased internet accessibility, has been crucial for AI development. The affordability of internet access and mobile devices, particularly in countries like India, has led to an unprecedented surge in data generation. The affordability of data played a crucial role, allowing for widespread video consumption and content creation. The evolution of GPU technology has provided the necessary computing power to process and analyze massive datasets. The Creator Economy and the Data Explosion The period between 2013 and 2020 witnessed an explosion in the creator economy , driven by increased data affordability and the accessibility of internet services. A significant portion (99%) of the world's data was generated within a short period (2015-2020), highlighting the rapid growth of digital content. The shift towards entertainment-focused internet usage (90% of users) further contributed to the data surge. Generative AI and Large Language Models The abundance of data has fueled the development of large language models (LLMs) like GPT, which are trained on massive datasets encompassing text, code, and other forms of information. Generative AI differentiates itself from traditional AI by its ability to produce new content, such as text, images, and code, based on user prompts. This contrasts with traditional AI, which primarily focuses on analysis and decision-making. The emergence of platforms like Midjourney and Stable Diffusion has popularized AI-powered image generation. The Impact of Generative AI and Future Trends The rapid adoption of generative AI tools, such as ChatGPT and DALL-E, demonstrates their transformative potential. ChatGPT's user growth within its first few months surpassed that of Instagram and TikTok. The increasing demand for powerful GPUs is driving technological advancements in this field. Higher-end versions are needed to handle the processing demands of advanced models. The cost of accessing powerful AI tools, like the pro version of certain platforms, is a factor to consider. Key Takeaways The evolution of AI has been significantly shaped by the availability of data and computing power. The rise of the creator economy and the widespread adoption of internet services have fueled the data explosion, which, in turn, has enabled the development of sophisticated AI models like large language models and generative AI tools. These advancements are transforming various aspects of our lives, from content creation to artistic expression, and are poised to continue shaping the future. Sam Altman has expressed concerns about the potential misuse of powerful AI models. He highlights several areas of risk: Misuse by individuals: There's a concern that very powerful models could be misused in significant ways, such as for creating new forms of bioterror or posing substantial cybersecurity challenges. ( ) Loss of control: Models capable of self-improvement could potentially lead to a loss of control. ( ) Disinformation: The spread of disinformation is another area of concern. ( ) Agentic AI: AI systems that can take actions, like clicking around on a computer or the internet, present a significant safety challenge. If these AI agents make mistakes, the stakes are much higher, especially if they have access to personal systems and information. ( , ) To address these concerns, OpenAI has safety measures in place: Preparedness Framework: OpenAI uses a "preparedness framework" to internally assess and check for risks before releasing models. ( ) Iterative Deployment and Feedback: They believe in an iterative process of deploying AI systems to the world while the stakes are relatively low. This allows them to gather feedback and learn how to build safer systems. ( ) Safety as a Core Product Feature: The view is that a good AI product is inherently a safe product. Users will only use AI agents if they can trust them not to cause harm, like mismanaging finances or deleting data. ( ) Focus on Trust: Building trust is crucial, especially as AI becomes more agentic and integrated into users' systems. ( ) Industry-wide Care for Safety: There's a general sentiment that most efforts in the AI field care deeply about AI safety and are trying to develop it responsibly. ( , ) Sam also previously proposed the idea of a safety agency that would license AI efforts, though his views on the specific implementation of such a policy have evolved as he has learned more about how government works. ( ) What other aspects of AI safety or OpenAI's approach are you curious about? Demis Hassabis has several main concerns regarding the development of Artificial General Intelligence (AGI): International Standards and Cooperation: A primary concern is the need for international standards and cooperation between countries, companies, and researchers as AGI development approaches its final stages. He emphasizes that AGI will affect everyone globally, so international agreement on how these systems are built, their goals, and how they are deployed is crucial. ( , , ) Societal Readiness: Hassabis expresses concern that society may not be fully prepared for the advent of AGI, which he believes could be 5 to 10 years away, or possibly sooner. ( , ) AGI Risk and Controllability: He is worried about the inherent risks of AGI as it becomes more autonomous and agent-based. Ensuring that humans can stay in charge of these systems, control them, interpret their actions, understand them, and implement effective, non-movable guardrails is a significant challenge. This includes the difficulty of controlling highly capable, self-improving systems. ( , , ) Misuse by Bad Actors: Like many in the field, Hassabis is concerned about the potential for AGI, as an unbelievably powerful dual-purpose technology, to be misused by bad actors for harmful purposes. ( , ) He believes that if these risks can be managed effectively, AGI can lead to an "amazing future." ( ) What are your thoughts on the feasibility of establishing effective international cooperation for AGI development? Google DeepMind CEO Worries About a “Worst-Case” A.I Future, But Is Staying Optimistic Demis Hassabis on AGI: A Structured Summary This blog post summarizes key points from an interview with Demis Hassabis, focusing on Artificial General Intelligence (AGI), its potential benefits and risks, and the implications for future generations. Introduction Demis Hassabis, co-founder of DeepMind (acquired by Google in 2014) and a 2024 Nobel Prize winner in Chemistry for his work on AlphaFold, discusses AGI – artificial general intelligence – and its potential impact on society within the next 5-10 years. He highlights the importance of international cooperation to navigate the development and deployment of this powerful technology responsibly. AGI: The Promise and the Challenge What is AGI? AGI refers to an AI system possessing human-level cognitive capabilities. It’s the long-sought goal of AI research since the 1950s, aiming to create general-purpose techniques applicable to a wide range of problems, unlike today's specialized AI systems. AlphaFold, which predicts protein structures, exemplifies the power of specialized AI but also highlights the potential of more general approaches. AGI's Potential Benefits: AGI could revolutionize various fields, including biology, medicine, and energy. Potential applications include: Curing diseases Developing new energy sources (fusion, batteries, superconductors) Addressing climate change Enabling unprecedented human flourishing and space exploration. From Specialized to General AI: Hassabis emphasizes that progress in AGI involves building upon general techniques and then adding specializations. DeepMind's work with neural networks, initially developed for games, demonstrates this approach, as it is being adapted for protein structure prediction and other fields. The development of foundational models capable of understanding language, images, video, and sound across multiple modalities is crucial for achieving AGI. The Risks of AGI Hassabis acknowledges the potential risks associated with AGI, drawing parallels to science fiction scenarios. He identifies two main categories of risk: Harmful Use by Bad Actors: The dual-use nature of AGI is a significant concern. Powerful technology can be repurposed for malicious purposes by individuals, rogue states, or other bad actors. Balancing the benefits of widespread access with the need to prevent misuse is a critical challenge. Loss of Control: As AGI systems become more autonomous and self-improving, there is a risk of losing control. Ensuring that these systems remain aligned with human values and goals, and that appropriate safeguards are in place, is paramount. This is a difficult challenge requiring substantial research and development. Risk Category Description Mitigation Strategies Harmful Use by Bad Actors Malicious use of AGI by individuals or groups for destructive or harmful purposes. International cooperation, strict regulations, robust security measures, and careful access control. Loss of Control AGI systems exceeding human control, potentially leading to unintended consequences or misalignment with values. Thorough testing, robust safety protocols, explainable AI, and mechanisms for human oversight and intervention. Preparing for an AGI-Driven Future The interview shifts to the societal implications of AGI and how to prepare for its arrival. Hassabis emphasizes the need for foresight and proactive risk assessment, acknowledging the uncertainty surrounding the timeline and potential impact. Adaptability: Humans are highly adaptable. Just as we adapted to previous technological revolutions (computers, internet, smartphones), we will adapt to AGI. Education and Skills: Hassabis suggests that learning programming and mathematics is beneficial, even if the nature of programming itself may change drastically with the advent of natural language programming. Parenting in the Age of AGI: He reflects on raising children in a world profoundly altered by AGI, emphasizing the importance of embracing change and learning to utilize these powerful tools effectively. Demis Hassabis: Scientist First, Entrepreneur Second The interview concludes with Hassabis clarifying his identity as a scientist first and foremost. He views AGI as the ultimate scientific tool, a means to address fundamental questions about the universe and intelligence itself. He sees himself as both a scientist and an entrepreneur, driven by a desire to use AGI to make a practical difference in the world. Key Takeaways Demis Hassabis's perspective on AGI highlights the enormous potential benefits while acknowledging the significant risks. International cooperation, responsible development, and proactive risk mitigation are crucial to ensure that AGI benefits humanity. Preparing for this transformative technology requires adaptability, continuous learning, and a focus on utilizing AGI's power for good. The future will likely see a radical shift in how we live and work, requiring individuals and societies to adapt and embrace the changes ahead. like? well, i think if you get that wrong, then um you know, you've got uh all these harmful use cases uh being done with uh these systems, you know, and that can range from uh um kind of doing the opposite of what of what we're trying to do without finding cures. you could end up finding, you know, toxins, these kinds of things with those same uh uh same systems. and so a lot of the cases, all the good use cases, if you invert the the goals of the system, uh you would get the the sort of harmful cases. um and as a society, we've got to this is why i've been sort of in favor of international cooperation around this because the systems wherever possibility 1 million extra users just with affordability explosion of people felt that there is a bit of robotic element to it right it's looking academic it's looking a little bit not sometimes plastic. right now we feel a very intelligent assistant trying to help and explain things right.00:53Facebook used IML a lot for what like to give you ad. which means that Facebook understands that this person is talking about Munar and potentially this person might be looking for a hotel in Munar in the next two weeks.01:10Hello everyone, this is C Laxman and welcome to another episode of possibility project and student possibly midle for maybe possible and I welcome Krishna Kumar. He's a prolearner in you know AI. He's been in the AI game for a long time and he's also an entrepreneur and he's been a speaker and you know multifaceted personality on he's the one uh who's going to be with us and then giving you everything about AI and welcome uh Kk to our episode and we also have you must have already seen him from our previous episode uh he's none other than nicl Bernard.02:14he's now uh an pro leader and he's also been uh you know with a big four firm uh been a consultant and he knows the in and out of industry and then how ai is going to revolutionize the industry so he's also there with us today to join our conversation and welcome nl to our episode. so let's [music] start until we had that coffee. probably favorite dialogue like today I'm going to talk about something that is going to be like the future in technology you will actually secure your future uh big data analytics, machine learning, blockchain technology, uh the whole game, one single word that we hear all along, ai, ai, artificial intelligence, okay.04:06charg basic understanding and for a very long time I thought AI means charg okay then something beyond gp conversation gp but then I thought that's what like you know 50 60% of AI probably would be that and anything built beyond GP.04:35I'm still believing that But then then we had this conversation, you know, I I occasionally see you posting about Jai and things like that. We had this conversation, then I understood there is so much about AI that I don't know of.05:11Okay. And I thought I am part of AI and most of my friends. So if I consider myself after the conversation to be at level 0.001, 001. okay,. I know just I just started using AI in terms of conversation.05:29But then you told me that there is so much things we even spoke about like, okay, single person I wanted to know like, you know, so where did all start history From where it all started? Right. Great. so AI, I started I mean, much before we actually heard about AI, right? So 1960 US artificial intelligence, which is very similar to human intelligence concept, a word like a word the term AI coined like in 1960s.06:12So um, I still remember one, um, test called Alan Turin test, right? Which essentially calls uh, human like voice, human and we are not able to distinguish between both. That's the moment of real AI, right? Um, now we have a concept called AI voice agent and a human voice agent, right? Oh, this is AI voice agent.06:44That means AI has not really matured or not really, uh, creator has not taken enough effort to make it humanlike, right?? so the idea is not to make it sound like AI. the idea is to make it sound like human.06:58right? now can AI become like human? this is where the real parity comes in. okay.. so now earlier concept of AI was this test is very simple. okay.. uh, two cabins are made. okay. one side the machine will chat with the human on the other side a real human will chat with the human on the other side.07:27okay. other people are feeling this is real human. and sometimes the human so that's why it's not able to distinguish indistinguishable real test. today we have reached a point. so we still in some people felt that there is a bit of robotic elem