Applications and future of AI in various industries

From: nikhil.kamath

Defining AI and Its Evolution

Artificial Intelligence (AI) can be understood through its historical development and different conceptual approaches to intelligence itself [00:11:20]. The pursuit of AI is driven by the obsession to uncover the mysteries of intelligence, with the belief that the only way to do so is to build intelligent machines [00:02:41]. The consequences of building such machines could be profoundly important for humanity [00:03:04].

Historically, the definition of intelligence within AI has been viewed from multiple perspectives, likened to the parable of the blind men and the elephant [00:11:20] [00:13:22].

• Reasoning and Search: One early approach (1950s) defined intelligence as the ability to reason and search for solutions to problems, such as the “traveling salesman problem” [00:11:46] [00:12:00]. This often involved optimization, finding the best solution among many possibilities [00:12:50]. This branch, known as “good old-fashioned AI” (GOFAI), was dominant until the 1990s [00:14:04] [00:29:00]. It included rule-based systems, logical inference, and heuristic programming (which avoided exhaustive searches) [00:18:27] [00:19:23] [00:29:00].
• Learning and Neural Networks: A parallel branch, also starting in the 1950s, focused on reproducing the learning mechanisms observed in animal and human brains [00:15:27] [00:15:43]. The idea was that intelligence emerges from networks of simple, connected elements that learn by modifying the strength of their connections (like neurons) [00:15:56] [00:16:05]. Early examples include the perceptron (1957) for recognizing simple shapes [00:21:00]. This approach was initially limited but led to fields like pattern recognition [00:16:57] [00:17:09].

Machine Learning and Deep Learning

Machine learning is a subfield of AI where machines are trained from data rather than being explicitly programmed [00:29:11] [00:31:42].

• Supervised Learning: The system is given an input and a desired output, and its internal parameters are adjusted to make its output closer to the desired one [00:27:36]. This process requires vast amounts of labeled data [00:27:52].
• Reinforcement Learning: The system is not given correct answers but rather feedback on whether its output was “good” or “bad” [00:32:23]. It’s highly inefficient for general tasks, requiring many trials, but excels in environments like games where systems can play millions of times against themselves (e.g., chess, Go, poker) [00:43:37] [00:43:43].
• Self-Supervised Learning: This modern approach, highly prominent in the last 5-6 years, bridges the gap between supervised and unsupervised learning [00:33:21]. Data itself is used to generate the “supervision” [00:34:31]. For example, by masking parts of a text or corrupting an image, the system is trained to predict the missing words or recover the original image [00:34:00] [00:34:40]. This means no manual labeling of millions of examples is needed [00:34:53].

Deep Learning and Large Language Models

Deep learning is a subcategory of machine learning that utilizes neural networks with multiple layers [00:28:28] [00:39:26]. The breakthrough in the 1980s was the ability to stack multiple layers of “neurons” (mathematical functions that compute weighted sums with a threshold) and train them using algorithms like backpropagation [00:39:26] [00:40:19]. This allowed for the computation of complex functions, overcoming the limitations of earlier perceptrons [00:28:02] [00:41:20].

Key architectural components of deep neural networks include:

• Convolutional Neural Networks (ConvNets): Inspired by the visual cortex, ConvNets excel at processing natural data like images and audio signals [00:45:12] [00:45:28]. They use “neurons” that look at small, local areas of the input, detecting motifs that are then integrated by subsequent layers [00:45:42].
• Transformers: A different architecture for arranging neurons, particularly effective for processing sequences of “tokens” (like words in text) [00:46:23]. Transformers are “equivariant to permutation,” meaning the order of input items doesn’t fundamentally change the output if permuted [00:46:47].

Large Language Models (LLMs), such as those powering chatbots like ChatGPT, are a special case of self-supervised, auto-regressive Transformers [00:36:46] [00:59:17]. They are trained to predict the next word in a sequence given the preceding words, using vast public text datasets from the internet [00:56:11] [00:56:23]. With billions of parameters, LLMs can store immense knowledge and manipulate language impressively, capturing grammar and syntax across multiple languages [00:56:52] [00:57:31].

Current Applications and Limitations of LLMs

LLMs excel at tasks involving language manipulation and information retrieval [00:57:31] [00:57:51]. They can answer questions, write essays, and even pass professional exams [00:56:46] [01:03:30].

However, LLMs have significant limitations:

• Discrete vs. Continuous Data: LLMs work best for text because text is discrete (finite number of words/tokens) [01:00:00]. They struggle with continuous, high-dimensional data like images and videos, where predicting every pixel is mathematically intractable [01:00:00] [01:05:52].
• Lack of Physical World Understanding: LLMs do not understand the physical world [01:02:07]. They can make “stupid mistakes” that reveal a lack of real-world comprehension [01:02:23]. This is why we have LLMs that can pass the bar exam but no fully autonomous self-driving cars or domestic robots that truly understand their environment [01:02:37].
• Limited Memory: LLMs primarily have two types of memory: inherent knowledge encoded in their parameters during training (like a human’s general understanding after reading a novel) and a limited working memory within their input prompt [01:03:26] [01:04:09]. They lack persistent, long-term memory similar to the human hippocampus [01:03:09] [01:04:26].

Future of AI

The next major challenge in AI is to build systems that can learn how the world works by observing videos and images [01:03:11]. This will require new architectures, different from the auto-regressive models used for LLMs [01:05:04].

Next-Generation Architectures

• Joint Embedding Predictive Architectures (JEPAs): This promising approach, being developed by researchers, aims to predict abstract representations of future video frames rather than every pixel [01:00:26] [01:01:19] [01:10:25]. By eliminating unpredictable details from the representation, systems can learn fundamental structures of the world by predicting what comes next in a video [01:11:47] [01:12:38].
• World Models: These models will be able to imagine the consequences of actions and plan complex sequences of actions hierarchically, making accurate short-term predictions while also enabling long-term, more abstract planning [01:13:35] [01:13:54]. This represents “system 2” reasoning (deliberate planning) as opposed to LLMs’ “system 1” reactive capabilities [01:08:07] [01:16:14].

Achieving human-level intelligence could potentially happen within a decade, though this is an optimistic timeline that assumes no unexpected obstacles [01:14:50]. It will require new architectures like JEPAs, not just scaling up current LLMs [01:15:57].

Applications of AI in Industries

The current and future of AI offer vast opportunities across various industries.

Business-to-Business (B2B) Applications

• Legal and Accounting: These sectors are ripe for disruption through AI, as highlighted by Bill Gates [01:23:57] [01:24:02].
• Business Information: Generating reports on competitive situations in specific market segments (e.g., fintech, finance) [01:24:14].
• Internal Information Systems: LLM-powered systems can provide quick answers to employee questions about administrative or company information, eliminating the need to search through multiple internal websites [01:24:26]. This area involves fine-tuning models for specific vertical applications [01:24:51].

Consumer-Oriented Applications

• Education: While not always highly lucrative without government contracts, education is a strong application area [01:25:02].
• Healthcare: LLMs can provide medical assistance, helping users understand symptoms and decide whether to seek professional medical help, especially in areas with limited access to doctors [01:25:10].
• Rural Areas and Literacy: AI assistants that can speak local languages and interact via speech can serve populations uncomfortable with literacy, opening up applications in agriculture and other sectors [01:25:48].
• Smart Devices: AI will increasingly integrate with devices like smartphones and smart glasses, enabling real-time translation and other helpful features [01:28:30] [01:29:02].

Investing and Entrepreneurship in AI

For entrepreneurs and investors, the current landscape is dominated by open-source foundation models like Llama [01:23:19]. The most viable business model involves taking an open-source model, fine-tuning it for a particular vertical application, and becoming an expert in that niche [01:23:40].

Key Trends for Investment:

• Open Source Dominance: The future of AI platforms will likely be open source, similar to Linux’s dominance in operating systems [01:26:48]. Open source offers portability, flexibility, and security, and its performance is catching up to proprietary engines [01:27:08].
• Distributed Training: Future AI models will be trained in a distributed fashion, not controlled by a single company [01:27:32]. This requires local computing infrastructure, making investments in data centers and local AI training capabilities crucial [01:19:17].
• Cost of Inference: The cost of AI inference is rapidly decreasing (by a factor of 100 in two years for LLMs) [01:20:25]. This drives wider deployment and requires significant innovation in hardware beyond what is currently dominated by companies like Nvidia [01:20:18].
• Data Quality: Collecting and filtering high-quality data will remain an expensive but crucial part of training models [01:17:01]. There is a need for more encompassing datasets that cover all the world’s languages, cultures, and value systems, which necessitates a collaborative, global effort [01:18:00] [01:18:33].

For individuals considering a career in AI, advanced studies like PhDs or Master’s degrees are highly recommended [01:21:25] [01:21:56]. These train individuals to invent new things, understand existing possibilities and limitations, and gain legitimacy for hiring talented people in this complex field [01:21:38] [01:22:06].

Impact on Human Intelligence and Society

The impact of AI on human intelligence and society is transformative. AI will amplify human intelligence, helping to solve many of the world’s problems, such as climate change, by enabling more rational decision-making and better mental models of how the world works [01:00:01] [00:09:56] [00:08:10].

Reshaping Human Roles and Jobs

• Shift in Tasks: Human intelligence will shift to different tasks [01:29:25]. AI will handle many “doing” tasks, allowing humans to focus on “deciding what to do” and “figuring out what to do” [01:29:40]. This means we will become “high-level managers” for AI systems [01:30:06].
• Enhanced Creativity and Productivity: AI will lift the abstraction level at which humans can operate, making us more creative and productive [01:31:17]. Just as calculators eliminated the need for complex mental arithmetic, AI will automate many current human tasks [01:31:01].
• Job Market Evolution: Economists predict that humans will not run out of jobs because we will not run out of problems [01:31:55]. Instead, AI will help us find better solutions to those problems [01:32:04].

Defining Intelligence in the AI Age

Intelligence can be defined as a combination of [01:32:11]:

• A collection of existing skills and experience in solving problems and accomplishing tasks [01:32:19].
• The ability to learn new skills very quickly with few trials [01:32:57].
• The ability to solve new problems “zero-shot,” without prior learning or similar experience, by using a mental model of the situation [01:32:24] [01:33:06].

Overall, the journey of AI is one of continuous discovery and evolution, pushing the boundaries of what machines can do and how they can augment human capabilities. The future of AI promises profound societal changes, transforming industries and redefining human roles.