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Six Problems Facing Driverless Cars and Their Track Record

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Six Problems Facing Driverless Cars and Their Track Record

Six Problems Facing Driverless Cars and Their Track Record

Driverless car technology is here to stay. Their track record so far is very impressive with Google cars driving over a million miles since 2009. During that time, 13 accidents have been recorded, but all of them were caused by other drivers or by human intervention. The robots themselves have been virtually flawless.

Right now, various vehicle manufacturers (Mercedes-Benz, General Motors, Toyota, Tesla, Audi, and more) have already built prototypes of driverless cars, and tech companies (Google, Uber, and potentially even Apple) are working on similar ambitions.

However, human nature seems to be innately suspicious of robots and artificial intelligence. Whether we’re talking about Skynet from “The Terminator” or Elon Musk’s concerns about AI, it’s clear that there will always be some pushback towards these kinds of ideas. It may take decades to convince people that autonomous cars will solve more problems than they create.

For these reasons, regulations around driverless cars are likely to move forward with a speed rivaling that of molasses. Policy makers do not want to be held responsible for potential hiccups, and much of the populace that is not in Silicon Valley will be slow to embrace ideas that could change the entire status quo.

Beyond general human suspicion of robots, there are some legitimate obstacles that need to be solved before driverless cars become a reality. Today’s infographic highlights some of these issues that need to be addressed:

Firstly, driverless cars struggle to identify humans alongside the vehicle or walking in front of them. This could lead to situations where they fail to see police officers, pedestrians, or workers on the side of construction zones giving instructions. Next, autonomous cars have a tough time in bad weather conditions in which an entire new array of problems are created for the algorithms to solve. Driving in Silicon Valley may be relatively straightforward, but what happens when a car encounters a snow storm in the Northeast, or torrential rainfall in the tropics?

It is also not a surprise that making decisions based on morality, ethics, and the law are also problems for robots. When a car needs to make a decision between running over a pedestrian, and getting rear-ended from behind, what does it do? Further, who is accountable in a situation where a car performs an emergency stop where it stops as fast as possible, but still hits another person or vehicle?

There are also some conflicting directives that may hinder decision-making by autonomous vehicles. They have a directive to avoid collisions with objects, but also to obey the law by staying in lanes. At what point, if ever, does one of these directives override the other?

Lastly, even with the above issues and regulations sorted, cost is going to continue to be preventative measure for many consumers. Even in 2025, it will cost up to an extra $10k in vehicle add-ons to allow for driverless software and hardware. This is expected to decrease as time goes on, with costs dropping to roughly $3k in 2035.

The good news for driverless technology is that most of the problems highlighted in today’s infographic can be overcome with more research and time. Even with these issues identified, autonomous cars are still driving today with relatively flawless track records compared to human-controlled vehicles. They eliminate human error, which is the cause of most accidents: robots don’t drive under the influence, use unnecessary speed, or get distracted by text messages.

The bad news for the technology? If you thought the lobby against Uber was strong, wait until all truck drivers, taxi drivers, public transit employees, and limousine drivers are collectively put up against a wall.

Original graphic by: ClickMechanic

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Nvidia Joins the Trillion Dollar Club

America’s biggest chipmaker Nvidia has joined the trillion dollar club as advancements in AI move at lightning speed.

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Nvidia Joins the Trillion Dollar Club

Chipmaker Nvidia is now worth nearly as much as Amazon.

America’s largest semiconductor company has vaulted past the $1 trillion market capitalization mark, a milestone reached by just a handful of companies including Apple, Amazon, and Microsoft. While many of these are household names, Nvidia has only recently gained widespread attention amid the AI boom.

The above graphic compares Nvidia to the seven companies that have reached the trillion dollar club.

Riding the AI Wave

Nvidia’s market cap has more than doubled in 2023 to over $1 trillion.

The company designs semiconductor chips that are made of silicon slices that contain specific patterns. Just like you flip an electrical switch by turning on a light at home, these chips have billions of switches that process complex information simultaneously.

Today, they are integral to many AI functions—from OpenAI’s ChatGPT to image generation. Here’s how Nvidia stands up against companies that have achieved the trillion dollar milestone:

Joined ClubMarket Cap
in trillions
Peak Market Cap
in trillions
AppleAug 2018$2.78$2.94
MicrosoftApr 2019$2.47$2.58
AramcoDec 2019$2.06$2.45
AlphabetJul 2020$1.58$1.98
AmazonApr 2020$1.25$1.88
MetaJun 2021$0.68$1.07
TeslaOct 2021$0.63$1.23
NvidiaMay 2023$1.02$1.02

Note: Market caps as of May 30th, 2023

After posting record sales, the company added $184 billion to its market value in one day. Only two other companies have exceeded this number: Amazon ($191 billion), and Apple ($191 billion).

As Nvidia’s market cap reaches new heights, many are wondering if its explosive growth will continue—or if the AI craze is merely temporary. There are cases to be made on both sides.

Bull Case Scenario

Big tech companies are racing to develop capabilities like OpenAI. These types of generative AI require vastly higher amounts of computing power, especially as they become more sophisticated.

Many tech giants, including Google and Microsoft use Nvidia chips to power their AI operations. Consider how Google plans to use generative AI in six products in the future. Each of these have over 2 billion users.

Nvidia has also launched new products days since its stratospheric rise, spanning from robotics to gaming. Leading the way is the A100, a powerful graphics processing unit (GPU) well-suited for machine learning. Additionally, it announced a new supercomputer platform that Google, Microsoft, and Meta are first in line for. Overall, 65,000 companies globally use the company’s chips for a wide range of functions.

Bear Case Scenario

While extreme investor optimism has launched Nvidia to record highs, how do some of its fundamental valuations stack up to other giants?

As the table below shows, its price to earnings (P/E) ratio is second-only to Amazon, at 214.4. This shows how much a shareholder pays compared to the earnings of a company. Here, the company’s share price is over 200 times its earnings on a per share basis.

P/E RatioNet Profit Margin (Annual)
Apple30.225.3%
Microsoft36.136.7%
Aramco13.526.4%
Alphabet28.221.2%
Amazon294.2-0.5%
Meta33.919.9%
Tesla59.015.4%
Nvidia214.416.19%

Consider how this looks for revenue of Nvidia compared to other big tech names:

For some, Nvidia’s valuation seems unrealistic even in spite of the prospects of AI. While Nvidia has $11 billion in projected revenue for the next quarter, it would still mean significantly higher multiples than its big tech peers. This suggests the company is overvalued at current prices.

Nvidia’s Growth: Will it Last?

This is not the first time Nvidia’s market cap has rocketed up.

During the crypto rally of 2021, its share price skyrocketed over 100% as demand for its GPUs increased. These specialist chips help mine cryptocurrency, and a jump in demand led to a shortage of chips at the time.

As cryptocurrencies lost their lustre, Nvidia’s share price sank over 46% the following year.

By comparison, AI advancements could have more transformative power. Big tech is rushing to partner with Nvidia, potentially reshaping everything from search to advertising.

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