State of AI: artificial intelligence needs more people and better data

Artificial intelligence (AI) is promising and increasingly ready for real-world businesses. But there is a talent shortage, a lack of diversity in this field, and concerns about the data processing that fuels increasingly sophisticated algorithms.

These are some of the observations of Nathan Benaich and Ian Hogarth, two important investors in the field of artificial intelligence, who published their fourth annual report “State of AI”, very dense, which reviews the evolution in this field during the past year .

While the report focuses on academic artificial intelligence and specific advancements in medicine and other fields, important developments have been raised for those looking to harness artificial intelligence and machine learning to promote creative smart businesses. “Insufficient AI alignment efforts by key organizations advancing the broader AI field, as well as concerns about the data sets used to train AI models and the bias benchmarks for evaluating models, they raise important questions about the best way to track the progress of AI systems with rapidly advancing capabilities, ”say Nathan Benaich and Ian Hogarth.

Here are some notable developments in AI over the past year:

  • AI is now part of important real-life scenarios, including application to critical infrastructure such as national power grids, automated supermarket storage optimization, drug discovery, and healthcare.
  • The “Transformer”, a deep learning model based on neural networks, has emerged as a general-purpose architecture for machine learning, increasingly applied to natural language processing and computer vision.
  • Other developments mentioned include increased self-supervision in the field of computer vision, which requires less training, and “textless” natural language processing based on “Generative Spoken Language Modeling” (GSLM), which enables ” learn representations of speech directly from raw audio data, without labels or text ”.
  • This year, artificial intelligence startups received record funding, and cybersecurity and data infrastructure companies that help companies adapt to the age of artificial intelligence were listed on the stock market.

AI Jobs

AI expertise is becoming a growing concern, as well as a field of opportunity. “Computer researchers, software developers, mathematicians, statisticians, and data scientists have seen their jobs grow far beyond that of the general workforce,” say Nathan Benaich and Ian Hogarth. “Computer Science and Engineering were the fastest growing university degrees between 2015 and 2018, representing 10.2% of all four-year degrees awarded in 2018. Their number increased by 34% and 25%, respectively, during this period, while the number of other titles granted increased 4.5% on average. “

Globally, Brazil and India are leading AI job growth, hiring more than three times more AI talent today than in 2017, matching or exceeding growth hiring from Canada and the United States, they add.

Data on diversity within American organizations differs dramatically between technical and non-technical teams, say the authors. Interestingly, globally, “nearly 30% of scientific research articles in India include female authors, compared to an average of 15% in the US and UK, and over 4%.% In China, “they add.

Data highlights

Venture capitalists highlight big data management concerns in the AI ​​world. “Careful data selection saves time and money by alleviating the challenges of big data. Working with massive data sets is cumbersome and expensive. Careful sample selection alleviates big data drawbacks by concentrating resources on the most valuable examples, but traditional methods often become impractical at scale. Recent approaches address these computational costs, allowing selection of data from modern data sets. “

Nathan Benaich and Ian Hogarth highlight the need to improve data quality, especially in real-time situations, such as detecting or predicting life-threatening events. They cite, for example, the threat of “data cascades”, defined by Google researchers as “compound events that cause negative effects after data problems.” These researchers warn “that current practices underestimate the quality of the data and lead to data cascades”, pointing to factors such as “lack of recognition of data work in AI, lack of adequate training, difficulty in accessing specialized data for the region / population studied “. Therefore, there is a need to “develop metrics to assess data quality, better incentives for data excellence, better data education, better practices for early detection of data cascades, and better access to data. the data”.

Investors also predict that the next year could see the launch of a general research company focused on artificial intelligence, “formed with significant support and an industry-focused vertical roadmap,” which could involve development tools or a life science app.

Source: .com

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