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AI vs. Humans: Which Performs Certain Skills Better?

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AI vs. Humans: Which Performs Certain Skills Better?

AI vs. Humans: Which Performs Certain Skills Better?

With ChatGPT’s explosive rise, AI has been making its presence felt for the masses, especially in traditional bastions of human capabilities—reading comprehension, speech recognition and image identification.

In fact, in the chart above it’s clear that AI has surpassed human performance in quite a few areas, and looks set to overtake humans elsewhere.

How Performance Gets Tested

Using data from Contextual AI, we visualize how quickly AI models have started to beat database benchmarks, as well as whether or not they’ve yet reached human levels of skill.

Each database is devised around a certain skill, like handwriting recognition, language understanding, or reading comprehension, while each percentage score contrasts with the following benchmarks:

  • 0% or “maximally performing baseline”
    This is equal to the best-known performance by AI at the time of dataset creation.
  • 100%
    This mark is equal to human performance on the dataset.

By creating a scale between these two points, the progress of AI models on each dataset could be tracked. Each point on a line signifies a best result and as the line trends upwards, AI models get closer and closer to matching human performance.

Below is a table of when AI started matching human performance across all eight skills:

SkillMatched Human
Performance
Database Used
Handwriting Recognition2018MNIST
Speech Recognition2017Switchboard
Image Recognition2015ImageNet
Reading Comprehension2018SQuAD 1.1, 2.0
Language
Understanding
2020GLUE
Common Sense
Completion
2023HellaSwag
Grade School MathN/AGSK8k
Code GenerationN/AHumanEval

A key observation from the chart is how much progress has been made since 2010. In fact many of these databases—like SQuAD, GLUE, and HellaSwag—didn’t exist before 2015.

In response to benchmarks being rendered obsolete, some of the newer databases are constantly being updated with new and relevant data points. This is why AI models technically haven’t matched human performance in some areas (grade school math and code generation) yet—though they are well on their way.

What’s Led to AI Outperforming Humans?

But what has led to such speedy growth in AI’s abilities in the last few years?

Thanks to revolutions in computing power, data availability, and better algorithms, AI models are faster, have bigger datasets to learn from, and are optimized for efficiency compared to even a decade ago.

This is why headlines routinely talk about AI language models matching or beating human performance on standardized tests. In fact, a key problem for AI developers is that their models keep beating benchmark databases devised to test them, but still somehow fail real world tests.

Since further computing and algorithmic gains are expected in the next few years, this rapid progress is likely to continue. However, the next potential bottleneck to AI’s progress might not be AI itself, but a lack of data for models to train on.

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Ranked: Semiconductor Companies by Industry Revenue Share

Nvidia is coming for Intel’s crown. Samsung is losing ground. AI is transforming the space. We break down revenue for semiconductor companies.

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A cropped pie chart showing the biggest semiconductor companies by the percentage share of the industry’s revenues in 2023.

Semiconductor Companies by Industry Revenue Share

This was originally posted on our Voronoi app. Download the app for free on Apple or Android and discover incredible data-driven charts from a variety of trusted sources.

Did you know that some computer chips are now retailing for the price of a new BMW?

As computers invade nearly every sphere of life, so too have the chips that power them, raising the revenues of the businesses dedicated to designing them.

But how did various chipmakers measure against each other last year?

We rank the biggest semiconductor companies by their percentage share of the industry’s revenues in 2023, using data from Omdia research.

Which Chip Company Made the Most Money in 2023?

Market leader and industry-defining veteran Intel still holds the crown for the most revenue in the sector, crossing $50 billion in 2023, or 10% of the broader industry’s topline.

All is not well at Intel, however, with the company’s stock price down over 20% year-to-date after it revealed billion-dollar losses in its foundry business.

RankCompany2023 Revenue% of Industry Revenue
1Intel$51B9.4%
2NVIDIA$49B9.0%
3Samsung
Electronics
$44B8.1%
4Qualcomm$31B5.7%
5Broadcom$28B5.2%
6SK Hynix$24B4.4%
7AMD$22B4.1%
8Apple$19B3.4%
9Infineon Tech$17B3.2%
10STMicroelectronics$17B3.2%
11Texas Instruments$17B3.1%
12Micron Technology$16B2.9%
13MediaTek$14B2.6%
14NXP$13B2.4%
15Analog Devices$12B2.2%
16Renesas Electronics
Corporation
$11B1.9%
17Sony Semiconductor
Solutions Corporation
$10B1.9%
18Microchip Technology$8B1.5%
19Onsemi$8B1.4%
20KIOXIA Corporation$7B1.3%
N/AOthers$126B23.2%
N/ATotal $545B100%

Note: Figures are rounded. Totals and percentages may not sum to 100.


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Meanwhile, Nvidia is very close to overtaking Intel, after declaring $49 billion of topline revenue for 2023. This is more than double its 2022 revenue ($21 billion), increasing its share of industry revenues to 9%.

Nvidia’s meteoric rise has gotten a huge thumbs-up from investors. It became a trillion dollar stock last year, and broke the single-day gain record for market capitalization this year.

Other chipmakers haven’t been as successful. Out of the top 20 semiconductor companies by revenue, 12 did not match their 2022 revenues, including big names like Intel, Samsung, and AMD.

The Many Different Types of Chipmakers

All of these companies may belong to the same industry, but they don’t focus on the same niche.

According to Investopedia, there are four major types of chips, depending on their functionality: microprocessors, memory chips, standard chips, and complex systems on a chip.

Nvidia’s core business was once GPUs for computers (graphics processing units), but in recent years this has drastically shifted towards microprocessors for analytics and AI.

These specialized chips seem to be where the majority of growth is occurring within the sector. For example, companies that are largely in the memory segment—Samsung, SK Hynix, and Micron Technology—saw peak revenues in the mid-2010s.


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