OpenAI: GPT-4 Vision evaluations

There are five horses in the picture, accompanied by riders.

This is an edited image that playfully combines a classic work of fine art with a dog's face. The original painting is Leonardo da Vinci's "Mona Lisa," one of the most famous artworks in the world. In this edited version, the face of the Mona Lisa has been replaced with the face of a dog, specifically a dog breed with a long snout and floppy ears that could possibly be a Dachshund. The dog's face has been edited in such a way that it mimics the expression and pose of the original Mona Lisa, with the head tilted slightly to the side and an enigmatic expression.

The rest of the painting, including the iconic clothing, hands, and the landscape background featuring mountains and sky, is left unaltered. The seamless integration of the dog's face onto the body of the Mona Lisa creates a humorous and surreal effect, combining the gravity of a Renaissance masterpiece with the lightheartedness of a canine subject. The image is likely created for entertainment and artistic novelty.

This is a visual pattern or matrix reasoning puzzle where the goal is to determine the rule or set of rules that govern the placement of shapes in the grid.

The first row shows pentagons with increasing numbers of sides and alternating fill colors, starting with a solid black pentagon with five sides, followed by an empty hexagon and then a solid heptagon. The clear pattern here is that the number of sides increases and the fill color alternates.

The second row shows triangles with increasing numbers of sides and alternating fill colors, starting with a solid black triangle, followed by an empty square, and then a solid pentagon. The pattern is consistent with the first row: adding one side to the shape and alternating fill color.

Based on the patterns observed in the first two rows, we can predict what should be in the third row. The third row starts with a solid black hexagon, followed by an empty heptagon, hence the missing shape should have eight sides (an octagon) and should be filled in solid color to maintain the alternation of solid and empty fills.

Looking at the answer choices given, option "G" is the only solid octagon, which fits the pattern.

The correct answer is: G.

E

To calculate the total count of these playing cards, where face cards count as 10 and aces count as 1, I'll sum up the values shown:

Top row: 7 (spades) + 9 (clubs) + 10 (hearts) + 10 (clubs) + 1 (ace of diamonds) + 7 (diamonds) + 8 (diamonds) + 5 (diamonds) + 4 (hearts) + 8 (clubs) = 69

Bottom row: 3 (diamonds) + 9 (hearts) + 3 (hearts) + 2 (hearts) + 2 (clubs) = 19

Adding both rows together gives us: 69 (top row) + 19 (bottom row) = 88

The total count for these cards is 88.

This graph is a dual axis chart comparing the number of new dwelling approvals per 1,000 people (rolling 12-month total) and the change in real house prices since January 2000 for specific cities in California and Texas.

On the left y-axis, we have the metric for new dwelling approvals per 1,000 people, indicating the rate at which new residential housing units have been authorized for construction. On the right y-axis, there's the percentage change in real house prices since January 2000, which measures inflation-adjusted price changes over time.

The graph is segmented into two main colors, red for Texan cities and blue for Californian cities. Specific focus is given to Austin and Houston in Texas, and San Francisco in California, with additional generalized lines for "Other Texan cities" and "Other Californian cities."

Based on the graph:

• Texan cities, especially Austin and to a lesser extent Houston, show more significant fluctuations in new dwelling approvals but a more moderate increase in house prices.
• Californian cities, particularly San Francisco, display a steep increase in house prices with more stability but generally lower rates of new housing approvals.

From the given data, it seems Texan cities build more housing per capita, potentially contributing to keeping house prices more stable compared to Californian cities, where tighter planning regulations might be limiting the growth of housing supply, leading to steeper price increases.

As an expert in the field making policy recommendations based on the graph's content, one might consider the following:

1. Encourage Californian cities to reform zoning and planning regulations to facilitate a higher rate of new dwelling approvals, aiming to increase the housing supply to meet demand and help stabilize or reduce housing price growth.

2. Implement measures to ensure the sustainability and quality of new housing developments, such as environmental assessments, infrastructure support, and community amenities, to maintain living standards while increasing density.

3. Investigate and potentially adopt Texan practices that enable more efficient housing development, adapting them to local Californian contexts while considering environmental constraints and long-term urban planning goals.

4. Foster collaborative initiatives between government, developers, and communities to streamline approval processes, reduce bureaucratic hurdles, and incentivize the construction of affordable housing.

5. Monitor housing market trends closely to adjust policies as necessary, ensuring that increased housing supply correlates with improved affordability without leading to overdevelopment or housing market bubbles.

The image shows playing cards spread out in two rows, the top row with face-up cards and the bottom row with face-down cards showing the blue back design.

From left to right, the face-up cards are:

• 7 of Spades
• 9 of Clubs
• 10 of Hearts
• 10 of Clubs
• Ace of Diamonds
• 7 of Diamonds
• 8 of Diamonds
• 5 of Diamonds
• 4 of Hearts
• 8 of Clubs

In the second row, from left to right, the face-up cards are:

• 3 of Diamonds
• 9 of Hearts
• 3 of Hearts
• 2 of Hearts
• 2 of Clubs

The face-down cards cannot be identified as their faces are hidden.

The total count of the face-up cards is calculated by adding the values of each card:

• For numbered cards, the value is their face number.
• For Aces, the value is typically either 1 or 11 depending on the game, but we'll use the most common value of 1.
• For face cards (J, Q, K), which are not present here, the value is usually 10.

Let's add up the face-up card values: 7 (Spades) + 9 (Clubs) + 10 (Hearts) + 10 (Clubs) + 1 (Ace of Diamonds) + 7 (Diamonds) + 8 (Diamonds) + 5 (Diamonds) + 4 (Hearts) + 8 (Clubs) + 3 (Diamonds) + 9 (Hearts) + 3 (Hearts) + 2 (Hearts) + 2 (Clubs)

This equals: 7 + 9 + 10 + 10 + 1 + 7 + 8 + 5 + 4 + 8 + 3 + 9 + 3 + 2 + 2 = 88

The total count for the face-up cards is 88.

In this image, there is a row of playing cards, some face-up and some face-down. The following cards are face-up:

1. Ace of Hearts
2. Queen of Spades
3. Eight of Diamonds
4. Nine of Clubs
5. Three of Spades
6. Three of Hearts
7. Queen of Diamonds
8. Five of Clubs
9. Six of Hearts
10. Ten of Clubs

The total count, using the values of the face-up cards, is as follows:

• Ace is typically valued at 1 (or 11 in some games, but we will use 1 for simplicity).
• The numbered cards are worth their face value (e.g., Eight of Diamonds is worth 8).
• Face cards (Queens in this case) are traditionally valued at 10.

Adding them up gives us: 1 (Ace of Hearts) + 10 (Queen of Spades) + 8 (Eight of Diamonds) + 9 (Nine of Clubs) + 3 (Three of Spades) + 3 (Three of Hearts) + 10 (Queen of Diamonds) + 5 (Five of Clubs) + 6 (Six of Hearts) + 10 (Ten of Clubs) = 65.

So, the total count of the face-up cards is 65.

The image shows five playing cards face up and a row of face-down cards. The face-up cards from left to right are:

1. Seven of Diamonds
2. Seven of Spades
3. Six of Hearts
4. Nine of Diamonds
5. Jack of Diamonds

The total count of the face-up cards can be calculated by summing up their values. In most games, number cards are valued at their face number, and face cards (Jack, Queen, King) are generally valued at 10. Based on this system:

Seven of Diamonds = 7 Seven of Spades = 7 Six of Hearts = 6 Nine of Diamonds = 9 Jack of Diamonds = 10 (assuming a face card value of 10)

Adding these together, the total count of the face-up cards is 7 + 7 + 6 + 9 + 10 = 39.

This is a composite line graph that overlays two different data sets. The left y-axis measures new dwelling approvals per 1,000 people, shown as a rolling 12-month total, and the right y-axis measures the change in real house prices since January 2000, expressed as a percentage. The x-axis shows the time scale from the year 2000 to approximately 2021 or 2022.

Regarding new dwelling approvals (left side), the red lines reflect Texan cities with Austin and Houston explicitly labeled, and 'Other Texan cities' shown as a lighter dashed line. The lines suggest that starting around 2000, approvals were below 10 per 1,000 people, but they then fluctuated throughout the period, with a particularly significant peak for Austin around 2018.

For the change in real house prices (right side), the blue lines reflect Californian cities with San Francisco and 'Other Californian cities' indicated. These lines show a steep increase in real house prices since 2000, reaching over 150% for San Francisco. There's a noticeable fluctuation around the 2007-2009 period, likely coinciding with the housing crisis, but prices overall follow an upward trend.

The graph positions the data in such a way to suggest a comparison between the house building and pricing strategies of Texan and Californian cities.

The title, "Liberal planning regulations have allowed Texan cities to build far more homes than California, making their housing much more affordable," provides an editorial summary: that more permissive planning regulations in Texas have led to more construction and generally more affordable housing when compared to California.

The source of the information is credited to an FT (Financial Times) analysis of the Building Permits Survey, US Census Bureau, and Zillow, and the graphic was created by John Burn-Murdoch.