The big comparison: which tech giant is most likely to win the race for AI dominance?
AI is the biggest topic of the moment and all the big techs want to grab their piece of the pie in this trend. Which one will succeed? Which one has the best chance and how are they doing?
What is the race for?
Artificial intelligence (AI) is a field that focuses on creating intelligent machines and software. Companies are currently battling for leadership in key areas that make up a huge pie that every company wants a bite of. And it's tempting! And in what areas is this race being fought?
- General AI: Achieving machines with human-level intelligence and general intelligence. That's still out of reach, but it's a long-term goal for tech companies. Google, OpenAI, DeepMind, etc. are engaged in this research.
- Special AI: Focused on achieving and surpassing human capabilities in specific domains such as playing Go (DeepMind), natural language processing (Google, Facebook), image recognition (Google, Facebook, NVIDIA), etc.
- Applied AI: Development of AI systems and algorithms that can be commonly used in business applications for automation, prediction, personalization and recommendation. Almost all major technology companies are active here.
- Ethics and Safety: Necessary research into the implementation of ethics and safety principles into the development of AI. Google (AI Principles), DeepMind (Responsible AI), OpenAI and Facebook (AI Ethics Board) are active here.
- Transparency and explainability: developing new techniques to ensure transparency and understanding of AI, algorithms and their decision-making. It is still an open and active field of research.
- Regulation: Potential regulatory frameworks applicable to AI. This is where technology companies and governments are struggling for appropriate standards and laws.
Recent earnings reports from major AI companies suggest that even the current challenging macroeconomic environment has not weakened their resolve to develop existing and new AI offerings. Companies continue to invest heavily in AI, with global private investment more than doubling year-on-year in 2021. Similarly, the number of patent applications nearly doubled in 2021 to 141,000 from the previous year. The hunger is enormous.
And who is doing best?
Alphabet $GOOG-0.3%
Alphabet
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It probably says it all that the company recently announced an internal "code red." In fact, they are missing the train on all tracks. Their search engine is under tremendous pressure from other companies and competitors. Their market dominance is in jeopardy.
CEO Pichai said on the earnings call that the company is making "good progress" towards their AI goals.
"We will continue to incorporate generative advances in AI to improve search in a thoughtful and deliberate way," Pichai said.
He said Google is using AI to improve ad conversion rates and to reduce the amount of ballast that goes into AI models. Pichai said that in addition to its own homegrown chips that power the models, it also uses processors from Nvidia.
which makes the vast majority of the graphics chips used to train and deploy cutting-edge AI. That computing power can be a big advantage.
AlphaGo is an artificial intelligence developed by DeepMind, a subsidiary of Alphabet. AlphaGo made headlines and news in 2016 when it beat the world's most intelligent Go player, Lee Sedol , in a five-game match . Go is a type of ancient Chinese board game that has simple rules but is incredibly complex and cannot be played without human intuition. The game has a huge number of moves that make it more complex and really hard or simply impossible for a machine to learn, as many scientists thought at the time.
However, AlphaGo has developed human-level intuition and is able to play the game more creatively than anyone has ever played it. This is made possible by a method of training machine learning models known as Deep Reinforcement Learning. The model is trained on a large corpus of human Go games and fine-tuned using Reinforcement Learning (trial-and-error learning). DeepMind claims that their AI is universal, meaning it can do much more than just play Go. Because of this, engineers have used it to regulate the cooling systems in Google's data centers and have also used it to solve other kinds of problems, such as protein folding, etc.
Amazon $AMZN+1.5%
Amazon
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Amazon is focusing on AI in three main areas:
Personalization and recommendation: Amazon has one of the most capable recommendation systems in the world, known as Amazon Recommendations. Using machine learning, it can personalize the customer experience and recommend relevant content, products and services. This technology is used across Amazon's web interface.
Logistics automation: Amazon has invested heavily in AI to manage its huge logistics centers and transportation network. It uses robots, demand prediction systems and route optimization to increase efficiency and reduce costs.
New products with AI: Amazon is launching a number of new products with AI, such as the Alexa virtual assistant, Echo smart speakers, Ring smart cameras, etc. These products help bring AI into the everyday lives of customers and allow Amazon to collect vast amounts of data to train its AI systems.
In addition to these core areas, Amazon is also engaged in research in the areas of ethical AI, transparent algorithms, and AI regulation. In recent years, it has invested in several research organizations focused on these issues.
On Thursday, Amazon CEO Andy Jassy gave an unusually long answer to an analyst's question about the company's plans for generative AI.
Jassy said Amazon is building its own LLM and designing chips for machine learning in data centers, stressing that the market is huge.
"These big language models, the ability for generative AI, have been around for a while. But frankly, just six to nine months ago, these models weren't that compelling," Jassy said. "They've gotten so much bigger and so much better so quickly that this really represents a remarkable opportunity to transform virtually every existing customer experience."
Jassy also said Amazon's size will allow it to become one of the few companies creating LLM, which can take hundreds of computers running for weeks, overseen by expensive machine learning engineers.
Meta $META+2.7%
Meta
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Meta (formerly Facebook) focuses on AI in three main areas:
Personalizing the user experience: Meta uses AI and machine learning to personalize the content users see in their news feeds and on platforms like Facebook, Instagram, and WhatsApp. The goal is to ensure that users see the most relevant and interesting posts and ads.
Content security and moderation: Meta has invested heavily in AI systems to detect and moderate harmful content such as misinformation, terrorism, hate speech and more. These systems help ensure a safe environment on their platforms, although their performance is still subject to criticism.
AI Ethics Research: Meta has established an AI Ethics Board to research ethical principles and frameworks for the responsible use of AI. The goal is to ensure that AI at Meta is developed and used responsibly and ethically. However, this board has also garnered controversy due to the composition of its members.
Meta is also investing in research into transparent algorithms, AI explainability and long-term safe artificial 'intelligence'. While their current AI portfolio is mostly at the applied level for content personalization and moderation, they seem to be trying to play an active role in the important ethical issues surrounding these technological developments.
Zuckerberg said that while the company has used machine learning to provide recommendations and power products such as Facebook's news feed or advertising systems, a new major area of focus is generative foundation models.
"We've made pretty amazing progress in this area, and the work that's happening now will impact every one of our apps and services," Zuckerberg said.
He said the company will be working on a variety of products using the technology, including chat experiences on WhatsApp and Facebook Messenger, tools to create images for Facebook and Instagram posts, and eventually programs that could spit out entire videos from short descriptions.
Microsoft $MSFT+1.6%
Microsoft
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Microsoft uses OpenAI's GPT technology in its Bing search engine, Office suite, and Teams teleconferencing system.
Microsoft is excited about the purchase. For example, it says downloads for Bing have quadrupled since Microsoft added the chatbot . Microsoft has created more than 200 million images thanks to the Bing integration.
But investors can't rejoice 100% yet. The CEO has warned that significant capital will be needed to build the huge data centres needed to run AI applications.
Microsoft is currently focusing on AI in three main areas:
Azure AI platform: Azure is a cloud computing environment that provides a wide range of AI services such as machine learning, deep learning, natural language processing, computer vision and more. These services can be used by internal Microsoft teams as well as external customers and partners. Azure AI is the market leader for enterprise AI deployments.
Applications and features with AI: Microsoft has integrated AI into many of its products, including Office 365, Dynamics 365, LinkedIn, etc. This includes features such as automatic content generation, personalized recommendations, predictive analytics and process automation.
Ethical AI research: Microsoft has created an AI and Ethics in Engineering and Research (AETHER) team to research ethical principles for AI development in line with the values of integrity, inclusion, transparency and fairness. They also work with several partner organisations, such as the Partnership on AI, to promote responsible AI.
Apple $AAPL+0.2%
Apple is specific. Everyone would expect such a giant to dominate. But in reality, it hasn't demonstrated anything substantial yet. It's still just continuing its original AI projects, which are:
Siri Assistant: Siri is Apple's voice assistant that is constantly learning and improving its capabilities through machine learning. Siri uses speech recognition, dialogue systems, and natural language processing to understand user commands and queries.
Face recognition and Touch ID: Apple uses Face ID and Touch ID to secure its devices and stream content. These biometric systems use machine learning and neural networks to identify users.
Personalizing recommendations: Apple uses data from its devices, services, and App Store to personalize content and app recommendations for each user. The goal is to provide the most relevant experience based on each user's interests and preferences.
Disclaimer: This is in no way an investment recommendation. This is purely my summary and analysis based on data from the internet and other sources. Investing in the financial markets is risky and everyone should invest based on their own decisions. I am just an amateur sharing my opinions.