Dallas, TX, United States, 11/11/2021 / Top Wire News Reporter /
The condition of artificial intelligence is promising, and it is becoming more ready for real-world applications. However, there are talent shortages, a lack of diversity in the field, and concerns about how to handle the data that fuels ever-more-sophisticated algorithms.
These are some of the findings of notable artificial intelligence investors Nathan Benaich and Ian Hogarth, who issued their fourth annual and densely packed “State of AI” report, which reviews progress in the field during the last year.
While the study concentrates on AI academia and particular breakthroughs in health and other sectors, there are crucial developments raised for those wishing to harness AI and machine learning to go forward in establishing intelligent organisations.
“The under-resourced AI-alignment efforts from key organizations who are advancing the overall field of AI, as well as concerns about datasets used to train AI models and bias in model evaluation benchmarks, raises important questions about how best to chart the progress of AI systems with rapidly advancing capabilities,” Benaich and Hogarth write.
The following are some notable Artificial intelligence – based developments in the last year:
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AI is currently being used in essential real-world scenarios such as national energy grids, automated supermarket warehouse optimization, drug development, and healthcare.
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“Transformers,” a deep-learning architecture based on neural networks, have developed as a general purpose architecture for machine learning, with expanding applications in natural language processing (NLP) and computer vision.
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Other advancements mentioned include the rise of self-supervision in computer vision, which requires less training, and “textless” natural language processing based on Generative Spoken Language Modeling (GSLM), which allows for the “task of learning speech representations directly from raw audio without any labels or text.”
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This year has seen record fundraising for AI startups, as well as IPOs for data infrastructure and cybersecurity firms that will assist organisations in retooling for the AI-first age.
AI talent is a growing concern, as well as an area of opportunity. “Computer research scientists, software developers, mathematicians, statisticians and data scientists saw an evolution of their employment that is far ahead of the general employed population,” Benaich and Hogarth state. “Computer science and engineering were the fastest growing undergraduate degrees over 2015 to 2018, accounting for 10.2% of all four-year degrees conferred in 2018. Their numbers increased by 34% and 25% respectively during the period, while the number of other awarded degrees increased 4.5% on average.”
Globally, Brazil and India are leading the way in AI job growth, recruiting more than three times as much AI talent now as they did in 2017, matching or exceeding hiring growth in Canada and the United States.
According to Benaich and Hogarth, gender and racial diversity data inside US firms varied dramatically across technical and non-technical teams. There is “a severe lack of female diversity in technical teams, whereas product and commercial teams attain a better balance.”
African Americans and Hispanics make up a smaller proportion of the AI workforce than they do in the broader labour force, with technical teams seeing the greatest disparity. These teams also have the greatest proportion of Asian employees. “Interestingly, on a global level, “almost 30% of scientific research papers from India include women authors compared to an average of 15% in the US and UK, and far greater than four percent in China.”
Concerns regarding managing huge data in the AI sector have been raised by venture investors. “Careful data selection saves time and money by mitigating the pains of big data. Working with massive datasets is cumbersome and expensive. Carefully selecting examples mitigates the pain of big data by focusing resources on the most valuable examples, but classical methods often become intractable at-scale. Recent approaches address these computational costs, enabling data selection on modern datasets.”
Benaich and Hogarth emphasise the need for higher data quality, especially in real-time circumstances such as identifying or anticipating life-threatening incidents. They mention, for example, the potential of “data cascades,” which Google experts define as “compounding events producing negative, downstream repercussions from data concerns.” These researchers warn that “existing procedures undervalue data quality and result in data cascades,” citing causes such as “lack of acknowledgment of data work in AI, insufficient training, and difficulties accessing specialised data for the researched region/population.” This asks for “the development of measures to quantify data goodness, stronger incentives for data excellence, greater data education, better methods for early identification of data cascades, and improved data availability.”
Source: Submit123News
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