THE EVOLUTION OF DEEP LEARNING
1950s:
Concept of artificial neural networks originates
2020: Major breakthroughs in AI-generated video
2023: Release of GPT-4
2015 – 2018:
Breakthroughs in generative facial images
Late 2022: Public inflection point with ChatGPT( powered by GPT-3.5)
“ We’ re way past the kid-in-thesweet-shop phase of picking tools to do stuff and experiment,” she said.“ I think we need to get into very detailed conversations around judiciously choosing what agents do and what agents don’ t do and why – and to pick up the same point is tokenomics. We’ re hearing that everybody is the beneficiary of new consumption based pricing contracts with Frontier Labs.”
As Hayley points out, a successful agentic enterprise is not defined by how many autonomous tools a company deploys but by how thoughtfully those agents are integrated into real business operations. Managing tokenomics – the underlying unit economics of token consumption and API costs – ensures that automated systems generate measurable return on investment rather than ballooning cloud expenditures.
Overall, deep learning has progressed quickly, from mathematical concepts in the mid-20th century to edge-based small language models, specialised enterprise SQL engines and autonomous agent networks.
As hardware acceleration continues to advance and open-weight models close the gap with frontier systems, the competitive advantage for modern organisations will no longer belong to those who simply adopt AI tools. Instead, it will belong to the enterprises that master applied deep learning by optimising infrastructure, fine-tuning models on proprietary context and judiciously deploying agents to solve real operational problems.
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