Investing in Artificial Intelligence (AI) – Everything You Need to Know – Securities.io
Posted: November 2, 2020 at 1:56 am
Artificial Intelligence (AI) is a field that requires no introduction. AI has ridden the tailcoats of Moores Law which states that the speed and capability of computers can be expected to double every two years. Since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a doubling every 3 to 4 months, with the end result that the amount of computing resources allocated to AI has grown by 300,000x since 2012. No other industry can compare with these growth statistics.
We will explore what fields of AI are leading this acceleration, what companies are best positioned to take advantage of this growth, and why it matters.
Machine learning is a subfield of AI which is essentially programming machines to learn. There are multiple types of machine learning algorithms, the most popular by far is deep learning, this involves feeding data into an Artificial Neural Network (ANN). An ANN is a very compute intensive network of mathematical functions joined together in a format inspired by the neural networks found in the human brain.
The more big data that is fed into an ANN, the more precise the ANN becomes. For example, if you are attempting to train an ANN to learn how to identify cat pictures, if you feed the network 1000 cat pictures the network will have a small level of accuracy of perhaps 70%, if you increase it to 10000 pictures, the level of accuracy may increase to 80%, if you increase it by 100000 pictures, then you have just increased the accuracy of the network to 90%, and onwards.
Herein lies one of the opportunities, companies that dominate the field of AI chip development are naturally ripe for growth.
There are many other types of machine learning that show promise, such as reinforcement learning, this is training an agent through the repetition of actions and associated rewards. By using reinforcement learning an AI system can compete against itself with the intention of improving how well it performs. For example, a program playing chess will play against itself repeatedly, with every instance of the gameplay improving how it performs in the next game.
Currently the best types of AI use a combination of both deep learning and reinforcement learning in what is commonly referred to as deep reinforcement learning. All of the leading AI companies in the world such as Tesla use some type of deep reinforcement learning.
While there are other types of important machine learning systems that are currently being advanced such as meta-learning, for the sake of simplicity deep learning and its more advanced cousin deep reinforcement learning are what investors should be most familiar with. The companies that are at the forefront of this technological advancement will be best positioned to take advantage of the huge exponential growth we are witnessing in AI.
If there is one differentiator between companies that will succeed, and become market leaders, and companies that will fail, it is big data. All types of machine learning are heavily reliant on data science, this is best described as a process of understanding the world from patterns in data. In this case the AI is learning from data, and the more data the more accurate the results. There are some exceptions to this rule due to what is called overfitting, but this is a concern that AI developers are aware of and take precautions to compensate for.
The importance of big data is why companies such as Tesla have a clear market advantage when it comes to autonomous vehicle technology. Every single Tesla that is in motion and using auto-pilot is feeding data into the cloud. This enables Tesla to use deep reinforcement learning, and other algorithm tweaks in order to improve the overall autonomous vehicle system.
This is also why companies such as Google will be so difficult for challengers to dethrone. Every day that goes by is a day that Google collects data from its myriad of products and services, this includes search results, Google Adsense, Android mobile device, the Chrome web browser, and even the Nest thermostat. Google is drowning is more data than any other company in the world. This is not even counting all of the moonshots they are involved in.
By understanding why deep learning and data science matters, we can ten infer why the companies below are so powerful.
There are three current market leaders that are going to be very difficult to challenge.
Alphabet Inc is the umbrella company for all Google products which includes the Google search engine. A short history lesson is necessary to explain why they are such a market leader in AI. In 2010, a British company DeepMind was launched with the goal of applying various machine learning techniques towards building general-purpose learning algorithms.
In 2013, DeepMind took the world by storm with various accomplishments including becoming world champion at seven Atari games by using deep reinforcement learning.
In 2014, Google acquired DeepMind for $500 Million, shortly thereafter in 2015 DeepMinds AlphaGo became the first AI program to defeat a professional human Go player, and the first program to defeat a Go world champion. For those who are unfamiliar Go is considered by many to be the most challenging game in existence.
DeepMind is currently considered a market leader in deep reinforcement learning, and Artificial General Intelligence (AGI), a futuristic type of AI with the goal of eventually achieving or surpassing human level intelligence.
We still need to factor in the other other types of AI that Google is currently involved in such as Waymo, a market leader in automonous vehicle technology, second only to Tesla, and the secretive AI systems currently used in the Google search engine.
Google is currently involved in so many levels of AI, that it would take an exhaustive paper to cover them all.
As previously stated Tesla is taking advantage of big data from its fleet of on-road vehicles to collect data from its auto-pilot. The more data that is collected the more it can improve using deep reinforcement, this is especially important for what are deemed as edge cases, this is known as scenarios that dont happen frequently in real-life.
For example, it is impossible to predict and program in every type of scenario that may happen on the road, such as a suitcase rolling into traffic, or a plane falling from the sky. In this case there is very little specific data, and the system needs to associate data from many different scenarios. This is another advantage of having a huge amount of data, while it may be the first time a Tesla in Houston encounters a scenario, it is possible that a Tesla in Dubai may have encountered something similar.
Tesla is also a market leader in battery technology, and in electric technology for vehicles. Both of these rely on AI systems to optimize the range of a vehicle before a recharge is required. Tesla is known for its frequent on-air updates with AI optimizations that improve by a few percentage points the performance and range of its vehicle fleet.
As if this was not sufficient, Tesla is also designing its own AI chips, this means it is no longer reliant on third-party chips, and they can optimize chips to work with their full self-driving software from the ground up.
NVIDIA is the company best positioned to take advantage of the current rise in demand in GPU (Graphics processing unit) chips, as they are currently responsible for 80% of all GPUsales.
While GPUs were initially used for video games, they were quickly adopted by the AI industry specifically for deep learning. The reason GPUs are so important is that the speed of AI computations is greatly enhanced when computations are carried out in parallel. While training a deep learning ANN, inputs are required and this depends heavily on matrix multiplications, where parallelism is important.
NVIDIA is constantly releasing new AI chips that are optimized for different use cases and requirements of AI researchers. It is this constant pressure to innovate that is maintaining NVIDIA as a market leader.
It is impossible to list all of the companies that are involved in some form of AI, what is important is understanding the machine learning technologies that are responsible for most of the innovation and growth that the industry has witnessed. We have highlighted 3 market leaders, many more will come along. To keep abreast of AI, you should stay current with AI news, avoid AI hype, and understand that this field is constantly evolving.
View post:
Investing in Artificial Intelligence (AI) - Everything You Need to Know - Securities.io
- Are we ready for bots with feelings? Life Hacks by Charles Assisi - Hindustan Times - December 12th, 2020
- What are proteins and why do they fold? - DW (English) - December 12th, 2020
- Are Computers That Win at Chess Smarter Than Geniuses? - Walter Bradley Center for Natural and Artificial Intelligence - December 4th, 2020
- An AI winter may be inevitable. What we should fear more: an AI ice age - ITProPortal - December 4th, 2020
- What the hell is reinforcement learning and how does it work? - The Next Web - November 2nd, 2020
- How to Understand if AI is Swapping Civilization - Analytics Insight - October 3rd, 2020
- In the Know - UCI News - October 3rd, 2020
- Test your Python skills with these 10 projects - Best gaming pro - October 3rd, 2020
- Is Dystopian Future Inevitable with Unprecedented Advancements in AI? - Analytics Insight - June 26th, 2020
- Enterprise hits and misses - contactless payments on the rise, equality on the corporate agenda, and Zoom and Slack in review - Diginomica - June 8th, 2020
- AlphaGo - Top Documentary Films - June 5th, 2020
- AlphaGo (2017) - Rotten Tomatoes - June 5th, 2020
- Why the buzz around DeepMind is dissipating as it transitions from games to science - CNBC - June 5th, 2020
- The Hardware in Microsofts OpenAI Supercomputer Is Insane - ENGINEERING.com - June 5th, 2020
- This A.I. makes up gibberish words and definitions that sound astonishingly real - Digital Trends - May 17th, 2020
- QuickBooks is still the gold standard for small business accounting. Learn how it's done now. - The Next Web - April 19th, 2020
- The Turing Test is Dead. Long Live The Lovelace Test - Walter Bradley Center for Natural and Artificial Intelligence - April 8th, 2020
- The New ABCs: Artificial Intelligence, Blockchain And How Each Complements The Other - JD Supra - March 14th, 2020
- Enterprise AI Books to Read This Spring - DevOps.com - March 14th, 2020
- Chess grandmaster Gary Kasparov predicts AI will disrupt 96 percent of all jobs - The Next Web - February 25th, 2020
- The top 5 technologies that will change health care over the next decade - MarketWatch - February 25th, 2020
- How to overcome the limitations of AI - TechTarget - February 20th, 2020
- Levels And Limits Of AI - Forbes - February 20th, 2020
- From Deception to Attrition: AI and the Changing Face of Warfare - War on the Rocks - February 20th, 2020
- I think, therefore I am said the machine to the stunned humans - Innovation Excellence - February 10th, 2020
- AI on steroids: Much bigger neural nets to come with new hardware, say Bengio, Hinton, and LeCun - ZDNet - February 10th, 2020
- Why The Race For AI Dominance Is More Global Than You Think - Forbes - February 10th, 2020
- Why asking an AI to explain itself can make things worse - MIT Technology Review - January 29th, 2020
- AlphaZero beat humans at Chess and StarCraft, now it's working with quantum computers - The Next Web - January 18th, 2020
- What are neural-symbolic AI methods and why will they dominate 2020? - The Next Web - January 18th, 2020
- What is AlphaGo? - Definition from WhatIs.com - December 22nd, 2019
- AI has bested chess and Go, but it struggles to find a diamond in Minecraft - The Verge - December 18th, 2019
- AI is dangerous, but not for the reasons you think. - OUPblog - December 18th, 2019
- The Perils and Promise of Artificial Conscientiousness - WIRED - December 18th, 2019
- DeepMind Vs Google: The Inner Feud Between Two Tech Behemoths - Analytics India Magazine - December 18th, 2019
- AlphaGo - Wikipedia - December 11th, 2019
- DeepMind co-founder moves to Google as the AI lab positions itself for the future - The Verge - December 11th, 2019
- Biggest scientific discoveries of the 2010s decade: photos - Business Insider - December 11th, 2019
- Facebooks Hanabi-playing AI achieves state-of-the-art results - VentureBeat - December 11th, 2019