• The institute, also known as Stanford HAI, debuted at the beginning of the year. The impact of technology on society is also researched, along with new AI techniques. It makes available a report on the AI Index every year.
  • PaLM, another Google model introduced last year, has a development cost of USD 8 million, according to Stanford University.

The most recent edition of the AI Index Report, which explores machine learning advancements over the previous year, was published recently by the Stanford Institute for Human-Centered Artificial Intelligence.

The institute, often known as Stanford HAI, officially debuted in the first quarter of 2019. It investigates novel AI techniques and the social effects of the technology. Each year, it publishes its AI Index Report.

The most recent version of the research, released recently, has more than 350 pages. It covers a wide range of subjects, such as the price of AI training, initiatives to lessen bias in language models, and the influence of technology on public policy. The report highlights several significant achievements over the past year in each area it surveys.

AI’s Advancements and Difficulties

Over the past year, the most cutting-edge neural networks have grown more complex. Stanford HAI cites the Minerva large language model from Google LLC as an illustration. The 540 billion parameter model debuted last June required nine times as much computing power to train as OpenAI LP’s GPT-3.

The rising cost of machine learning projects is a direct result of the expanding hardware requirements of AI software. PaLM, a different Google model launched last year, is estimated to have cost USD 8 million to build by Stanford HAI. That is 160 times more than GPT-2, which OpenAI launched in 2019 and served as GPT-3’s forerunner.

Although AI models are capable of much more than they were a few years ago, they nevertheless have limitations. These restrictions apply to numerous areas.

In a recent release, Stanford HAI highlighted a 2022 study that revealed some reasoning tasks are complex for advanced language models. Planning-intensive tasks are frequently among the most difficult for neural networks. Researchers found numerous AI bias instances in large language models and neural networks designed for image synthesis last year.

In 2022, efforts by researchers to remedy those problems came to light. In its paper released recently, Stanford HAI emphasized how a novel model-training methodology dubbed “instruction tuning” has demonstrated promise to reduce AI bias. Instruction training, introduced by Google in late 2021, entails rephrasing AI prompts to make them simpler for a neural network to understand.

New Use Cases

Researchers not only improved AI models last year; they also discovered new uses for the technology. Some of such applications resulted in scientific breakthroughs.

Google’s DeepMind machine learning division unveiled AlphaTensor, a brand-new AI system, in October 2022. The technique was developed by DeepMind researchers to perform matrix multiplications more quickly. Machine learning models frequently employ a mathematical operation called matrix multiplication to convert input into decisions.

According to Stanford HAI, last year saw scientists use AI to support research in various other fields. One endeavor showed how AI might be used to find newer antibodies. A neural network that can regulate the plasma in a nuclear fusion reactor was developed due to another research, which Google’s DeepMind also directed.

The Effects of AI on Society

The latest report from Stanford HAI devotes several chapters to discussing how AI affects society. Whereas large language models have only just come to the general public’s attention, AI is already impacting some fields.

About 2% of U.S. lawmakers’ proposed federal AI-related legislation was enacted in 2021. That percentage increased to 10% last year. Meanwhile, 35% of all AI-related state legislation was approved in 2022.

The education industry is also experiencing the effects of machine learning. As of 2021, 11 nations will have adopted and implemented a K–12 AI curriculum, according to Stanford HAI’s research. Between 2010 and 2021, the proportion of new computer science Ph.D. graduates from American colleges with an AI focus nearly quadrupled to 19.1%.