Artificial Intelligence

Computers can process information much faster than humans but can they think? The concept of artificial intelligence goes back to the 1950s and Alan Turing (inventor of the computer system that cracked the German Enigma Code). Turing wondered if we would ever create machine that could “reason at the level of a human being.” The “Turing test” is that “computers need to complete reasoning puzzles as well as humans in order to be considered thinking in an autonomous manner.”

A more current view is that in order to have artificial intelligence at computer system must display intentionality, intelligence, and adaptability.

Intentionality

A key feature of the algorithms that make up artificial intelligence is that they allow computers to make decisions. They do this by having access to vast amount of digital information, having remote sensors, and having incredibly fast processing power. Thus such a computer system can process huge amounts of information almost instantaneously to make real world decisions. The use of artificial intelligence in self-driving cars is a prime example.

Self-Learning (Intelligence)

Artificial intelligence systems learn from their mistakes and can correct their programming to avoid making the same errors again. Such systems can keep learning and improving their performance. They can spot relevant information within huge amounts of data and make predictions that even intelligent humans would find hard to make. Such systems require intelligent programming as well because the system needs useful input to make useful predictions.

Adaptability

A key feature of artificial intelligence is that the smart, decision-making, and self-learning system can adapt. They learn from their own experience and this takes them beyond what the programmer or database might have predicted.

(Brookings Institute, What Is Artificial Intelligence?)

Self-Driving Vehicles

The reason why artificial intelligence can be applied to self-driving vehicles to day is because of the speed of data processing, size of databases, improved remote sensors, and programming that ties all of this together. The programming sets the parameters for the autonomous vehicle and then it learns “on the road.”

The Data Driven Investor writes about artificial intelligence and autonomous vehicles.

The automotive AI market reported that it is expected to be valued at $783 million in 2017 and expected to reach close to $11k million by 2025, at a CAGR of about 38.5%. IHS Markit predicted that the installation rate of AI-based systems of new vehicles would rise by 109% in 2025, compared to the adoption rate of 8% in 2015. AI-based systems will become a standard in new vehicles especially in these two categories:

• Infotainment human-machine interface, including speech recognition and gesture recognition, eye tracking and driver monitoring, virtual assistance and natural language interfaces.
• Advanced Driver Assistance Systems (ADAS) and autonomous vehicles, including camera-based machine vision systems, radar-based detection units, driver condition evaluation and sensor fusion engine control units (ECUs).

Deep learning technology, which is a technique for implementing machine learning (an approach to achieve AI), is expected to be the largest and the fastest-growing technology in the automotive AI market. It is currently being used in voice recognition, voice search, recommendation engines, sentiment analysis, image recognition and motion detection in autonomous vehicles.

So, how can you invest in artificial intelligence? We recently asked if it is time to buy GM based on their moving into electric and autonomous vehicles. We noted in that article that the self-driving car market could be worth as much as $7 Trillion by the middle of the century!

The Many Applications of Artificial Intelligence

Investor’s Business Daily writes about artificial intelligence stocks and notes, as we did, that although there are few companies that are strictly AI investments there are lots of companies applying the technology to their products and services. Among the many companies under Nasdaq you will find ones that will be powerhouses in AI.

It’s no secret that Alphabet (GOOGL), Microsoft (MSFT), Facebook (FB) and Amazon.com (AMZN) are all spending big bucks on AI technology. The tech giants are putting AI in consumer products and services, such as voice-activated smart home devices. Amazon, Google and Microsoft also are pushing AI technology into cloud computing.

Other companies highlighted in the article are IBM, Accenture, Epam Systems, Adobe Systems, Salesforce.com, Trade Desk, MTCH, IAC, Five9, Nvidia, Fortinet, Palo Alto Networks, VISA, and MasterCard.

The point is that artificial intelligence will have many possible applications. And, it will be how effectively a company applies the technology that will make the difference for the investor.

Selling Picks and Shovels Instead of Digging for Gold

A famous observation from the days of the 19th century California Gold Rush was that you were more likely to prosper selling picks and shovels to eager miners than by digging for gold yourself.

This thought may apply to the field of artificial intelligence as well. Applications of this advanced technology require lots and lots of advanced chips. The leader in this area so far is Nvidia but companies like Tesla are now starting to design and manufacture their own chips.

Forbes reported on why Tesla dropped Nvidia’s AI platform last year and replaced it with its own.

According to Musk, the Nvidia Drive PX2 computing platform – with one Pascal GPU and 2 Parker processors or CPUs – currently used in Tesla’s custom autonomous computerAutopilot Hardware 2.5 can process 200 frames a second, compared to “over 2,000 frames a second” with full redundancy and fail-over with Tesla’s designed computer.

Nvidia supplies chips for the likes of Mercedes and Honda. To the extent that a maker of self-driving cars wants to buy their chips instead of building that technology from the ground up, they will use someone like Nvidia, a leader in the field.