The Evolution of Machine Intelligence

What is AI

The term AI has been around for a while and is an umbrella, that includes many computer science fields pursuing a goal of making machines to perform tasks commonly associated with intelligent beings. The definition is so broad, however, that we don’t find it practical to use. Instead, we look at machine capabilities as a spectrum — moving from simple, narrow, and straightforward tasks to more complex and fuzzy.

AI Applications

AI adoption started from tasks with a narrower scope and higher tolerance for mistakes, like recommenders (showing “people also buy” widget, or choosing the next post in the social network feed), dictation, OCR, basic image recognition. Though such tasks used to be performed by hard-coded algorithms, the ANN approach demonstrates much better results.

Boosting current software capabilities in every field.

Just like in the examples above ANN techniques are replacing hard-coded algorithms in every industry from healthcare to military, improving data analysis, simulations, data transformation, and more. It includes industrial machinery and infrastructure, where the next generation chips and firmware will incorporate ANNs.

Augmenting knowledge workers/co-creation.

Early results show that ANN techniques can be used to enable co-creation where humans direct the machine and edit the output. There are early examples in co-writing, co-coding, co-composing, co-designing, co-science, and so on.

Augmenting physical labor.

The physical world is a challenging environment. While more narrow tasks, like keeping the car in a lane or picking a product from a box seem solvable, it is less clear if the current hardware and AI techniques will be enough to fully solve autonomous vehicles, drones, and more versatile robots.

Market size

Based on our estimates, we believe the overall global market for the AI systems to be on the order of $10T broken down into the four areas below:

  • Boosting current capabilities: ~$1T
  • Physical labor: ~ $5T (ground transportation ~ $1T, physical labor in mining, construction, manufacturing, wholesale and retail trade, transportation and warehousing ~ $4T).
  • Knowledge workers: ~$4T.
  • AI infrastructure: hardware, tools, and services to build/run AI systems (~$1T).

The AI system stack

Below are the main components of the stack.

  • Talent. The engineers and researchers. But also the headhunters, outsourcing shops, freelance marketplaces, and other services that help connect these experts with the work.
  • Hardware. The TPUs, GPU, and CPU are currently the main sources of computing power for ANNs, while novel technologies, like neuromorphic chips, are being developed. Initially, the hardware was configured and set up in-house, though currently developers increasingly leverage capacity available in the public cloud.
  • Architectures. Since ANNs are not programmed but trained to perform specific actions, the job of the engineer is to come up with the most efficient architecture of the different types of ANNs (e.g. CNNs, transformers) and symbolic structures (e.g. tree search) based on the task at hand. The building blocks and reference architectures are developed by the open-source community, academia, and businesses, lead by the big technology companies (Google, Meta, Microsoft, Amazon, and others).
  • Data. Can be collected and labeled in-house and/or purchased from 3rd parties. The data can also be artificially generated to cover cases that are not well represented in the existing datasets. While there are open source datasets, the data availability became one of the most important bottlenecks for the open-source/academia researchers (the other one is access to powerful hardware).
  • Tooling. Software that helps build and manage AI applications. Tools for data management, labeling, deploying, analytics, etc. Also, services that collect or label data for clients.
  • Model-as-a-Service (MaaS). While some app developers will design and train the ANNs from scratch, many will use MaaS, where the pre-trained ANN (model) is available and ready to be used with limited fine-tuning. The model does all the processing heavy lifting, while the app collects the data and presents the output in the form (or a set of actions) needed by the customer. It is especially relevant when the model is very large (e.g. GPT-3 or PaLM) and it would be impractical to deploy it separately for each app/customer. Leveraging MaaS also dramatically reduces time-to-market. The most advanced systems leverage multiple MaaS offerings from different vendors to achieve the best results.
  • Application. Performs desired by customer actions, delivers end-user value.
  • Integration. Implementation of application in the customer-specific environment, integration with other systems, including other apps. Fine-tuning pre-trained ANNs and MaaS using customer-specific data (e.g. adjusting the general language translation service to the customer-specific terminology), ongoing work with the customer data, and AI system monitoring to make sure it performs as planned.
  • Embodiment. Sensors, manipulators, locomotion, etc. for the systems interacting with the physical world (EVs, drones, robots).

Known challenges of AI systems and areas of research

While modern AI systems demonstrate quite impressive results in tasks ranging from image recognition to playing Go and even explaining jokes, these are still the early days, and there are multiple areas for improvement, including abilities to better generalize, learn from fewer samples, process multiple types of data, and better energy efficiency. Many of them are interconnected and will likely require innovation at the levels of systems architecture, scale, and hardware architecture to achieve breakthroughs.


We expect these multi-year trends to shape the evolution of AI systems.

  • Bigger models and more powerful hardware enabling more advanced actions performed by AI systems.
  • Multimodality — the ability to process and relate information from multiple modalities, like text, audio, visual, etc. Models like Dall-E, Imagen, and NUWA are steps in this direction.
  • Embedding AI in every device powered by a chip.
  • Power law distribution of vendors in hardware (chips), MaaS, and edge autonomous software (powering drones and robots). A handful of companies will control most of the market.
  • Maturation of DataOps/MLOps allowing for AI applications at scale in enterprise.
  • Embedding AI into all modern software already used by businesses and consumers.
  • Explosion of applications built on top of MaaS, where developers benefit from continuous improvements of MaaS and leverage multiple vendors for the optimal results.
  • Transition from modular to end-to-end architectures.
  • Expansion of real-time AI.
  • Co-creation in copywriting, animation, music, computer games, and other creative tasks.
  • Declining costs of industrial and service robots, including Robots-as-a-Service / Pay-as-you-use models and growing supply of low-cost robots.
  • Growing robot density in the industry (current world average is 126 robots/10,000 employees, while in South Korea the number is 932). Gradual penetration of services.
  • China leadership in AI. Scientists from the Chinese Academy of Science, Peking, and Tsinghua Universities are already competitive with the oldest and best universities in the world: Oxford, Cambridge, Harvard, Stanford. Overall, CCP considers AI a critical area for China’s global leadership. China is also by far the largest destination for industrial robots (48% of global installs in 2020).



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