Brief History & Recent Developments of AI
Last updated
Last updated
As NodeGPT sets out, to build an infrastructure to continue the development of AI innovations, it’s essential to look back to history, so that we can see how far it has come and the obstacles it has overcome to extend its current frontier.
The origin of AI can be traced back to the mid-20th century, although the concept of intelligent systems can be found much earlier in history through mythology, literature. The formal development of AI as a field of study began in 1956 when John McCarthy coined the term "Artificial Intelligence" during a conference at Dartmouth College.
Initial AI researches were focused on creating systems that could perform tasks considered to require human intelligence, such as playing chess, solving mathematical problems, and reasoning about intellectual concepts. A lot of optimism was invested in developing AI to solve logical reasoning problems, leading to the creation of early systems like the Logic Theorist or McCarthy's Lisp programming language that provided essential tools for exploring these ideas. The Logic Theorist could prove statements from Principia Mathematica, and with that Lisp became one of the primary languages for AI research due to its symbolic processing capabilities.
These early successes with AI still faced some major challenges as researchers began to encounter some limitations to the technology. The complexity of real-world problems turned out to be far more challenging for AI systems to manage than initially expected. At some point, researchers were skeptical about the viability of AI becoming a successful technology.
With the resurgence in AI research due to its significant limitations, expert systems became prevalent. These expert systems used rule-based programming to simulate the decision-making abilities of human experts and found commercial success in industries like medicine and finance. This period also witnessed advances in machine learning, a subfield of AI focused on developing systems that can learn from data, rather than relying on explicit programming.
The mid-2000s saw the emergence of deep learning, a subset of machine learning that relies on artificial neural networks with many layers. Neural networks had been around since the 1950s, but their practical applications were limited by the lack of computational power and data. With the advancement in graphics processing units (GPUs) and the availability of massive datasets, it was possible to train much deeper neural networks. This led to significant breakthroughs in areas like image recognition, natural language processing, and game-playing.
One of the key moments in the rise of deep learning came in 2012, when a team from the University of Toronto led by Geoffrey Hinton used a deep neural network to win the ImageNet competition, which set a benchmark for image recognition. This achievement demonstrated the power of deep learning and sparked a wave of interest in the technology from both academia and industry. Big companies like Google, Facebook, and Microsoft began investing heavily in AI research which led to rapid advances in the field.
Another major milestone for AI came with Google’s deepmind AlphaGo program, which defeated the Go world champion player, Lee Sedol. Go is said to be a complex board game with more possible moves than there are atoms in the universe and has been considered a major challenge for AI due to its complexity. This victory showed a stunning demonstration of the capabilities of deep learning and reinforcement learning, another key technique in modern AI.
AI systems became more powerful, deploying in a wide range of applications such as NLP, models like OpenAI's GPT, and Google's BERT that revolutionized the ability of machines to understand and generate human language. These models are based on transformer architectures, allowing AI systems to achieve futuristic results in tasks like translation, summarization, and question-answering. In 2020, GPT-3 was released by OpenAI becoming one of the largest and most powerful language models ever created, with 175 billion parameters. This AI model can generate coherent and contextually relevant text on a wide range of topics, marking a significant leap in AI's ability to process language.
AI has made giant strides in fields like robotics, healthcare, finance, transportation, and blockchain. Autonomous vehicles being developed by Tesla and others are powered by AI systems that can process vast amounts of sensor data in real time. AI-powered tools are being increasingly integrated into industries like finance for fraud detection, risk assessment, and algorithmic trading. The rise of AI has also led to the development of new business models, with companies offering AI-as-a-service platforms that allow other organizations to leverage AI without needing to develop their own systems from scratch.
The fusing of Blockchain and AI is an area of growing interest, as these two fields complement each other in ways that enhance security, automation, transparency, and efficiency. The blockchain offers a decentralized and secure way of storing and verifying data while AI provides powerful tools for processing, analyzing, and making decisions based on that data. AI has been implemented in smart contracts to make them more intelligent and adaptable, making smart contracts that are more dynamic and responsive to external data, versatile in complex or unstable environments.
AI has found further use cases in blockchain, particularly in areas of data security and privacy, as one of the main challenges of AI is the need for large amounts of data to train machine learning models. The sharing of sensitive data in the centralized ecosystem can pose some privacy risks. This is where the decentralized nature of the blockchain converges to mitigate these risks and provide a solution by enabling secure data sharing without the need for intermediaries. AI can be applied to encrypted datasets stored on the blockchain, ensuring that models are trained on data without directly accessing the raw information. Other areas in the blockchain where AI has found use cases include;
DeFi - providing sophisticated risk assessments, automating portfolio management, and even executing trades through intelligent algorithms.
Tokenization of AI models - Using decentralized platforms to allow users to buy, sell, or rent AI models as tokens, enabling a broader distribution of AI capabilities.
The potential of AI in blockchain is vast, but the challenges associated with its implementation are equally substantial. AI has found a variety of use cases in the centralized world and is still finding its feet in the small but fast-growing decentralized ecosystem, which is why we’re building solutions for builders to leverage on and create monumental AI systems.
NodeGPT acknowledges the challenges builders face with creating AI models, stemming from insufficient computational power and its solution is a seamless fix for every party involved. You might be wondering, why NodeGPT is different from other cloud computing providers, this is because NodeGPT is decentralized and is powered by a community of contributors rather than a single corporation.
