The State of AI Report 2023, encapsulates vital AI advancements in research, industry, and politics.
It underscores GPT-4’s triumph, NVIDIA’s ascension to a $1T market cap due to GPU demand, and the proliferating open-source AI community with 32M LLaMa model downloads on Hugging Face in a month.
While Chat-GPT emerges as a developer favorite, GenAI’s shaky retention rates hint at potential adoption hurdles.
The report also delves into LLM’s impact on traditional industries like pharma and defense, but also healthcare and robotics (e.g., Google RT-2) alongside its geopolitical implications exemplified by the AI-driven chip wars and evolving global governance discussions.
The narrative concludes with a set of foresighted predictions for the AI landscape.
Before we start, let’s have a look at one of the last slides of the report — predictions! 🦉
The State of AI Report 2023 provides a comprehensive analysis of the current landscape of artificial intelligence (AI) in various domains, including research, industry, safety, and politics. In its sixth year, the report aims to distill the key themes and ideas that have emerged in the AI ecosystem.
One of the notable developments in 2023 was the emergence of Large Language Models (LLMs) as a dominant force in the AI field. OpenAI’s GPT-4, in particular, garnered significant attention by surpassing other LLMs in both classic AI benchmarks and human-designed exams. This achievement showcased the remarkable capabilities of LLMs and their potential impact on various applications.
However, the report also highlights a shift away from openness in AI research, driven by concerns related to safety and competition. While OpenAI published a limited technical report for GPT-4, other companies like Google and Anthropic provided even less information for their respective models.
Nonetheless, Meta AI and other organizations are actively contributing to the open-source community by releasing competitive LLMs that can match the capabilities of previous models like GPT-3.5.
Open-source AI models such as Llama and Llama 2 have gained significant traction, as evidenced by the increasing downloads and model submissions on platforms like Hugging Face. The popularity of these models reflects the vibrant and collaborative nature of the AI community.
I have written a blog tutorial explaining the graceful scaling properties of Llama 2 and other large language models:
🧑💻 Recommended: Llama vs Llama 2 – Still No Sign of Saturation!
In addition to LLMs, researchers have explored the potential of small language models trained on specialized datasets.
Surprisingly, these smaller models have demonstrated the ability to rival much larger competitors, suggesting the importance of dataset quality in AI training.
The demand for computational power in AI research has significantly benefited hardware companies like NVIDIA. Their GPUs have become a staple in AI research, with their chips being used extensively compared to alternative options. Even older GPU models, like the V100 released in 2017, continue to be widely utilized in AI research papers.
The competition among chip manufacturers has led to the development of special, sanctions-compliant chips for the Chinese market. Companies like NVIDIA, Intel, and AMD are adapting to geopolitical considerations and tailoring their offerings to meet the specific needs of their Chinese customers.
Furthermore, the report highlights the rapid growth of Chat-GPT, an internet product that has gained popularity among developers. This AI-powered tool has become a go-to resource for developers seeking coding solutions, gradually replacing traditional platforms like Stack Overflow.
Here’s an interesting visual showing the most interesting applications for most people in generative AI: Medicine grows fastest!
Overall, the State of AI Report 2023 provides valuable insights into the advancements, challenges, and trends shaping the AI landscape. It offers a glimpse into the dynamic nature of AI research, the evolving industry landscape, and the importance of safety, governance, and open collaboration in the AI community.
It even shows how LLMs impact the robotics industry — check out our Finxter article for more:
An interesting chart is given in the report that examines the popularity of LLM terms such as Reinforcement Learning Through Human Feedback (RLHF):
I also found this slide interesting that discusses the context size of several LLMs, it’s one of the most important metrics appreciated by users. Unfortunately, the long context windows don’t work nicely “in the middle”:
Frequently Asked Questions
Top Generative AI Applications in 2023
Generative AI is a subset of artificial intelligence that involves using machine learning models to generate new data. In 2023, the top generative AI applications include image and video generation, language translation, and music and art creation. These applications are being used in a variety of industries, including entertainment, marketing, and e-commerce.
Here’s the slide that shows how the Google Model Med-PaLM 2 disrupts medicine!
Key Findings in the McKinsey State of AI Report
The McKinsey State of AI Report for 2023 highlights the explosive growth of generative AI tools. The report found that one-third of survey respondents say their organizations are using generative AI regularly in at least one business function. The report also found that businesses increasingly invest in AI talent and infrastructure to support AI initiatives.
If you want to be on the right side of change, master this technology now!
Predictions for AI Adoption in the USA by 2023
According to a report by PwC, the adoption of AI in the United States is expected to increase significantly by 2023. The report predicts that AI will contribute $15.7 trillion to the global economy by 2030, with the United States being one of the biggest beneficiaries. The report also predicts that AI will create millions of new jobs in the United States, particularly in the healthcare and education sectors.
Key Takeaways from the Deloitte State of AI Report
The Deloitte State of AI Report for 2023 highlights the importance of ethical considerations in the development and deployment of AI. The report found that businesses are increasingly focused on building AI systems that are transparent, explainable, and accountable. The report also found that businesses are investing in AI talent and infrastructure to support AI initiatives.
Prompt engineering is shown to significantly impact the quality of responses:
Current State of Artificial Intelligence in 2023
In 2023, artificial intelligence is being used in a wide range of industries, including healthcare, finance, and retail. AI is being used to automate routine tasks, improve efficiency, and provide new insights and opportunities for businesses.
🧑💻 Recommended: 20 Ways to Make Money with GPT-4
Also make sure to check out our suite of prompt engineering courses on the Finxter Academy like this one:
Prompt Engineering with Llama 2
💡 The Llama 2 Prompt Engineering course helps you stay on the right side of change. Our course is meticulously designed to provide you with hands-on experience through genuine projects.
You’ll delve into practical applications such as book PDF querying, payroll auditing, and hotel review analytics. These aren’t just theoretical exercises; they’re real-world challenges that businesses face daily.
By studying these projects, you’ll gain a deeper comprehension of how to harness the power of Llama 2 using 🐍 Python, 🔗🦜 Langchain, 🌲 Pinecone, and a whole stack of highly ⚒️🛠️ practical tools of exponential coders in a post-ChatGPT world.
While working as a researcher in distributed systems, Dr. Christian Mayer found his love for teaching computer science students.
To help students reach higher levels of Python success, he founded the programming education website Finxter.com that has taught exponential skills to millions of coders worldwide. He’s the author of the best-selling programming books Python One-Liners (NoStarch 2020), The Art of Clean Code (NoStarch 2022), and The Book of Dash (NoStarch 2022). Chris also coauthored the Coffee Break Python series of self-published books. He’s a computer science enthusiast, freelancer, and owner of one of the top 10 largest Python blogs worldwide.
His passions are writing, reading, and coding. But his greatest passion is to serve aspiring coders through Finxter and help them to boost their skills. You can join his free email academy here.