Artificial Intelligence

Degree of Difficulty: Assessing When Machines Will Match Human Skills

The rapid pace of artificial intelligence (AI) development has led many to wonder when machines will match or surpass human capabilities across different domains. While AI has made huge strides in recent years, some skills remain incredibly difficult to automate. Understanding the key elements that make certain human skills challenging to replicate can provide insight into the trajectory of AI progress.


The idea of intelligent machines matching or exceeding human skills invokes both awe and apprehension. On one hand, advancements could free humans from dull or dangerous work and augment our capabilities. On the other, it raises concerns about displacement of jobs and ways AI could be misused if unchecked.

As AI capabilities grow, it’s important to evaluate different skills objectively, weighing both the technical complexity required and the nuanced understanding needed to master them. Appreciating the degree of difficulty can illuminate where machines may complement humans versus replace them in the nearer term. It also highlights areas requiring more research and development investment to achieve human-level proficiency.

This article examines key factors that make some human capabilities easy, moderate or difficult to automate. It provides examples across domains like sensory perception, language use, creative thinking, social skills and general intelligence. While the pace of progress makes firm predictions impossible, analyzing the complexity provides perspective on the trajectory of AI advancement.

Why Assessing Degree of Difficulty Matters

Predicting which human skills will be automated next and the timeline for achieving that is more art than science currently. AI systems rely on data, code and compute power, while human cognition emerges from brains containing billions of neurons and synapses. Comparing the two can feel like contrasting apples and oranges.

However, evaluating the complexity of different capabilities provides a framework for anticipating where machines may match humans first and areas requiring more fundamental breakthroughs. Along with technical difficulty, factors like economic incentives and potential benefits or risks also influence development priorities.

Appreciating the nuances of different skills allows us to imagine productive partnerships between humans and AI, combining complementary strengths. Tasks requiring flexibility, general knowledge, planning, creativity and social skills seem further from automation, for instance. Monitoring degree of difficulty over time also allows us to responsibly plan for potential workforce impacts as machines take on more roles.

Key Factors Influencing Degree of Difficulty

Several core elements determine how challenging any human skill is to automate with AI:

  • Sensory capabilities – The richness of sensory input required and difficulty processing raw perception into useful representations.
  • Physical capabilities – The dexterity, navigation and manipulation of objects needed to perform skills.
  • Knowledge requirements – The breadth and depth of knowledge about the world needed to demonstrate the skill.
  • Cognitive complexity – The reasoning, strategic planning, problem-solving, decision making and mental simulation involved.
  • Creativity – The skill to imagine novel solutions, insights or works based on experience.
  • Social intelligence – The ability to interact naturally with emotional humans following social norms.
  • General intelligence – The skill to transfer learning across different domains and adapt quickly.

Machines can now match or exceed human capabilities in areas requiring large amounts of data and pattern recognition, but struggle with skills needing flexible reasoning, planning, creativity and contextual understanding. Next we’ll examine specific examples that illustrate the spectrum of difficulty.

Examples of Skills by Degree of Difficulty

1. Easy for Machines

Some capabilities that pose little difficulty for AI with current methods include:

  • Structured data analysis – Finding patterns and making predictions from large structured datasets. AI excels at numerical analysis and optimization.
  • Game playing – Mastering games like chess or Go by searching possible moves and recognizing strong positions. AI can evaluate more options faster than humans.
  • Content generation – Producing original text or image content constrained by formats, topics and lengths. AI can generate coherent outputs thanks to advances in language models.
  • Object recognition – Identifying objects in images and video reliably by comparing to labeled data. Neural networks now surpass humans at image classification.
  • Speech transcription – Converting speech to text by learning mappings between sounds and words from samples. AI can transcribe speech continually with over 90% accuracy.

In these areas, raw computational speed or pattern matching on large training datasets enable AI systems to meet or surpass human capability levels. Next we’ll examine skills posing moderate technical challenges.

2. Moderate Difficulty

Some capabilities requiring more contextual understanding, planning and reasoning that pose moderate challenges include:

  • Content summarization – Producing abbreviated summaries conveying key points of documents or speech input. More difficult than generation alone given the abstraction required.
  • Machine translation – Translating text or speech from one language to another different language. Current systems still make subtle errors and struggle with rare phrases.
  • Anomaly detection – Identifying unusual data points, events or behaviors that differ from the norm. Performance depends heavily on the quality of training data.
  • Predictive maintenance – Forecasting maintenance needs of machines based on sensor data, usage patterns and repair history. Requires substantial domain experience to operationalize.
  • Autonomous vehicles – Navigating roads safely while following traffic laws and social conventions. Mastering driving in diverse conditions remains difficult.

For these skills, current AI methods start to falter without more explicit programming, common sense knowledge, reasoning ability, and deeper understanding of context and causality. Next we’ll examine areas posing even greater challenges.

3. Difficult for Machines

Some capabilities involve such complex physical, creative, social and general skills that they remain difficult for AI systems to achieve, including:

  • Fine motor skills – Handling and manipulating objects with human-level dexterity and precision. Require advanced robotic bodies and control algorithms.
  • Strategic planning – Defining long-term goals, generating options, and assessing risks and rewards to shape a course of action. Involves creativity and foresight current AI lacks.
  • Writing novels – Producing fictional stories with coherent plots, rich characters, and natural dialog. Beyond rote generation and requires creativity.
  • Complex problem solving – Developing novel solutions to problems not seen previously or with ambiguity. Involves perspective taking, abstraction and deduction.
  • Leadership – Making critical decisions under uncertainty and motivating coordinated action by communicating purpose and empathy. Necessitates social skills and judgment.
  • General intelligence – Applying knowledge across different contexts and quickly learning new skills with minimal data like humans. Remains an elusive benchmark for AI.

Mastering these capabilities requires breakthroughs across areas like unstructured perception, reasoning, knowledge representation, creativity, planning, social intelligence and transfer learning.

While existing methods have limitations, accelerated progress could yield surprises. Some skills like strategic planning seem within reach if current tech like deep learning and reinforcement learning are combined and scaled up with sufficient data. However, capabilities like leadership, creativity and general intelligence appear to require paradigm shifts given the lack of proven approaches so far.

Timeframes for Achieving Different Skill Levels

Given the spectrum of difficulty, we can consider rough potential timelines for when machines may reach different skill levels outlined below.

  • Narrow AI – Systems that match or exceed human capability in specific domains like games, pattern recognition, generation, etc. using current methods – achieved already in some areas, more emerging in 5-10 years.
  • General AI – Systems demonstrating human range of cognitive abilities like reasoning, knowledge, planning, learning etc. across most domains. Likely 10-30 years away.
  • Super AI – Systems significantly surpassing human-level skill across all domains including social skills, creativity, general intelligence, etc. At least 20-50+ years away without fundamental conceptual leaps in algorithms and compute.

However, these timeframes are moving targets given the pace of progress. They may shift significantly based on breakthroughs, funding, compute growth, availability of data, and level of effort dedicated to different capabilities. Social factors also influence development roadmaps and adoption curves.

Continued analysis of degree of difficulty and benchmarks tracking performance on skill tests over time will help calibrate these windows. There are likely upper bounds on speed given constraints like training costs, algorithmic innovations required, and availability of human knowledge for encoding. Transparency about factors influencing timelines can inform responsible planning and governance.

Assessing AI Through Degree of Difficulty Lens

Evaluating technical difficulty provides a principled way to track AI capabilities and anticipate timeframes for achieving different skill levels. Major areas of assessment include:

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  • Available training data – More and higher quality data expands capabilities by exposing systems to more examples to learn from and generalize. Data hungry techniques like deep learning are accelerating progress in some areas.
  • Compute power – Faster processors and larger neural networks expand model capabilities. However, returns are diminishing, and new algorithms must be more efficient.
  • Algorithmic advances – Novel machine learning techniques (e.g. transformers) expand abilities on certain tasks but may have limitations on others. More fundamental progress needed for reasoning, planning, etc.
  • Benchmark performance – Standardized tests to measure progress on skills related to language, common sense reasoning, robotics, creativity etc. Help calibrate models and identify strengths vs. weaknesses.
  • Expert evaluations – Demonstrations and comparisons of AI capabilities by researchers help assess nuanced aspects like social intelligence, generalizability, and ethics. Provide insight into limitations.

Multidimensional analysis combining technical benchmarks, expert reviews, and monitoring of key enablers illuminates both current abilities and gaps to be addressed. Evaluation principles and processes will enable responsible development.

The Path Forward

Assessing degree of difficulty provides perspective on the timeline and trajectory of AI systems achieving different human skill levels. While future progress is uncertain, analyzing the complexity of tasks and specifying evaluation criteria anchor projections.

Monitoring benchmarks and thoughtfully assessing capabilities as they emerge will enable us to maximize benefits and anticipate challenges. AI has already shown it can enhance human skills in many domains; clarifying limitations guides efforts to responsibly address them.

By combining technical rigor with nuanced social discussions, we can craft policies ensuring AI reflects the full richness of human values as capabilities advance. With prudent governance and progress at a human pace, increasingly intelligent machines can hopefully amplify our potential.


Q: Which human skills seem farthest from being automated with AI?

A: Skills requiring significant creativity, general intelligence, reasoning, strategic planning, leadership, complex social skills and dexterous physical capabilities seem most difficult to automate currently based on degree of difficulty. Progress is gradual on capabilities that need flexible cognition versus pattern recognition. Creative arts, advanced sciences, management, care giving, and craftsmanship are examples of skill areas farther from automation.

However, the landscape could shift if new techniques emulate higher level cognition rather than brute force statistical learning. Evaluating conceptual leaps, social adaption and new risks will be ongoing challenges.

Q: What are the biggest technical obstacles to developing human-level AI?

A: Several inter-related challenges make developing AI with more generalized human cognitive abilities difficult, including:

  • Acquiring common sense reasoning and world knowledge humans implicitly gain through experience.
  • Transferring learning between different tasks and contexts flexibly like people.
  • Achieving robust performance outside training distributions and handling novelty.
  • Developing efficient learning algorithms not as reliant on massive data.
  • Creating good internal representations of conceptual knowledge.
  • Building secure systems that align with ethics and social norms.

Overcoming these obstacles to create adaptable, trusted AI requires algorithmic innovations, infrastructure for accumulating knowledge, and responsible development practices.

Q: How long do experts estimate it will take to develop advanced AI with capability comparable to humans?

A: In a recent survey, AI researchers estimated a 50% probability of AI reaching human performance across all tasks by 2057, though with high uncertainty. While narrow AI capabilities will continue growing, researchers expect human-level general intelligence to take decades more given technical challenges. However, timelines could substantially accelerate or decelerate depending on breakthroughs. Ongoing assessment of progress and capabilities relative to benchmarks helps gauge status rather than solely relying on estimates.

Q: What are the best ways to measure AI capabilities compared to humans?

A: A multifaceted approach provides the most complete picture:

  • Quantitative benchmarking on domains like vision, language, reasoning using standardized tests.
  • Evaluations by subject matter experts across focus areas like creativity, empathy, dexterity.
  • Competitions where humans and AI perform limited tasks and are judged.
  • Interactive demos for nuanced capabilities like conversation agents.
  • Longitudinal studies in complex simulated or real world environments.

Evaluation principles like relevance, rigor, diversity, transparency and ethics are also important for measuring progress responsibly.

Q: How will degree of difficulty assessments shape planning and policies for AI development?

A: Analyzing the technical challenges and trajectory of different capabilities will enable proactive governance and planning:

  • Clarifying where human abilities likely won’t be exceeded soon can allay unfounded fears about AI and identify fruitful partnerships.
  • Appreciating nuances aids responsible development and evaluation frameworks.
  • Timeline estimates, even if uncertain, allow anticipation of impacts on employment, education, infrastructure.
  • Gaps between human skills and AI abilities illuminate priorities for technical development and investment.

Updating degree of difficulty assessments periodically provides perspective on progress to guide policy. However, technical advances alone don’t determine societal impacts – our values and choices shape how humanity benefits.

Q: What are positive ways for humans to work with increasingly capable AI systems?

A: Some principles for productive collaboration as AI capabilities grow:

  • Focus AI on enhancing human potential versus wholesale replacement. Keep roles requiring general intelligence, creativity, empathy.
  • Develop AI solutions collaboratively between technologists and domain experts.
  • Ensure transparency in AI capabilities and limitations to build appropriate trust.
  • Help the public understand basics of AI through education and interactive demos.
  • Implement governance frameworks addressing ethical development, security, privacy and impact on employment.
  • Encourage diversity – social, regional, gender, discipline – in designing and deploying AI.

Responsibly co-evolving human skills and AI capabilities can amplify our problem solving and provide fulfilling work. Any job displacement can be mitigated via training, assistance, and the freed time AI gives us. With wisdom and foresight, increasingly intelligent machines can enrich our lives.

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