Right now at the start of 2023, I would probably highlight 3 AI trends: generative models, automation, and legislation. Before we get into those specific topics let’s zoom out for just a second and look at two different reports you could read to get a sense of what is going on right now. One of the great places to start would be with the recently released 2023 AI Index report from the Institute for Human Centered AI.
Nestor Maslej, Loredana Fattorini, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Vanessa Parli, Yoav Shoham, Russell Wald, Jack Clark, and Raymond Perrault, “The AI Index 2023 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2023. https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf
You can look around the website they have setup here:
https://hai.stanford.edu/research/ai-index-2023
If the 386 page PDF seems like a lot of content to consume, then feel free to read the slimed down 2023 state of AI in 14 charts that they also produced
https://hai.stanford.edu/news/2023-state-ai-14-charts
The other interesting report that I read recently was the Google Cloud 2023 Data and AI Trends Report which you can download by giving them your email. That step seemed annoying so I’m going to leave the link to the report below, but it might default back to the information page
https://services.google.com/fh/files/misc/data_and_ai_trends.pdf
Those two reports aside, let’s jump into the 3 topics that will comprise the bulk of this missive.
Generative models - generative models are probably the biggest thing happening at the start of 2023. We are setting chat based and image based models like GPT-4 and DALL-E 2 from OpenAI [1]. One of the things spinning out of the generative models are the low code, no code, and copilot efforts. This is one of the areas where you will find a lot of coverage in the media as the generative models are being used as search engines and the image generating efforts are so easy to use people have gone thumbnail happy using them recently [2].
Automation - We are going to see a tremendous amount of better automation out of the new technology that is available [3]. I’m tempted to mention that I consider a lot of the copilot efforts to be a part of early automation. Those copilot efforts will move from being inline assistive to probably a more prompt based delivery. That type of request and receiving copiloting is what I would consider a form of automation.
Legislation - Countries are wholesale banning ChatGPT [4]. A lot of legislation will probably come into being related to trying to ensure safety [5]. I’m not going to spend a ton of time on this trend as it's going to unfold in a pretty predictable way.
You might find that this week the large language model from OpenAI has produced a better post than what I crafted. To that end, let's find out now what the ChatGPT model GPT-4 produces after being prompted about this one…
As of April 2023, the top trends in AI include the following:
Federated Learning: This approach allows AI models to be trained on decentralized data while maintaining privacy. It has gained prominence as a way to harness the potential of data from various sources without compromising user privacy.
AI Ethics and Regulation: As AI systems become more integrated into everyday life, there is an increasing focus on the ethical implications and regulatory measures required to ensure AI is used responsibly.
Explainable AI (XAI): With the growing complexity of AI models, there is a rising demand for models that provide interpretable and understandable explanations for their predictions or decisions.
AI in Healthcare: AI applications in healthcare are expanding rapidly, from diagnosis and treatment recommendations to drug discovery and personalized medicine.
AI for Climate Change: AI techniques are being used to tackle climate change by optimizing energy consumption, forecasting extreme weather events, and modeling climate patterns.
Edge AI: As IoT devices proliferate, there is a growing interest in running AI models on-device (at the edge) rather than relying on cloud-based processing. This approach can improve latency, privacy, and energy efficiency.
AI for Cybersecurity: AI is being deployed to enhance cybersecurity measures by detecting and preventing potential threats and attacks using advanced pattern recognition and anomaly detection.
Reinforcement Learning (RL): RL, a type of machine learning that focuses on decision-making, continues to gain traction as researchers explore its potential applications in various fields, including robotics, finance, and gaming.
Natural Language Processing (NLP): NLP techniques continue to advance, enabling more sophisticated AI applications in areas like language translation, sentiment analysis, and chatbot development.
AI and Robotics: AI is playing a key role in the development of advanced robotics systems, allowing for improved autonomy, decision-making, and adaptability in various domains, including manufacturing, logistics, and personal assistance.
These trends are expected to evolve as AI research and development continues to advance, addressing the opportunities and challenges associated with the integration of AI into various aspects of human life.
Links and thoughts:
Footnotes:
[1] https://openai.com/
[4] https://www.cnbc.com/2023/04/04/italy-has-banned-chatgpt-heres-what-other-countries-are-doing.html
What’s next for The Lindahl Letter?
Week 121: Considering an independent study applied AI syllabus
Week 122: Will AI be a platform or a service?
Week 123: Considering open source AI
Week 124: Profiling OpenAI
Week 125: Profiling Hugging Face (open and collaborative machine learning)
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That one with an obligatory AI trend’s post