Machine Learning Statistics

Machine Learning Statistics 2023: Facts about Machine Learning outlines the context of what’s happening in the tech world.

LLCBuddy editorial team did hours of research, collected all important statistics on Machine Learning, and shared those on this page. Our editorial team proofread these to make the data as accurate as possible. We believe you don’t need to check any other resources on the web for the same. You should get everything here only 🙂

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Top Machine Learning Statistics 2023

☰ Use “CTRL+F” to quickly find statistics. There are total 47 Machine Learning Statistics on this page 🙂

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Machine Learning “Latest” Statistics

• In 2006, the media-services provider Netflix held the first “Netflix Prize” competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%.^[1]
• In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software.^[1]
• Deep learning approaches account for 40% of the yearly value provided by analytics, according to McKinsey.^[2]
• Google’s machine learning powered lipreading algorithm outperforms a skilled human lipreader with a 46.8% accuracy rate.^[2]
• In at least one business function, 50% of respondents claimed that their organizations had used AI. (McKinsey, 2020).^[2]
• According to 61% of marketers, AI is the most important component of their data strategy. (Max G, 2019).^[2]
• Voice assistants are used on smartphones by 74.7% of customers aged 30 to 44 and 68.8% of consumers aged 45 to 60.^[2]
• The estimated size of the US deep learning software market by 2025 is $80 million.^[2]
• Advancements in AI and machine learning have the potential to increase global GDP by 14% from now until 2030.^[2]
• According to Statista (2021), among the biggest challenges to machine learning adoption include scaling up (43%), versioning of ML models (41%), and getting senior buy-in.^[2]
• The accuracy of machine learning approaches used to forecast COVID-19 patient death was 92%.^[2]
• Only 14.6% of companies claimed to be using AI capabilities in regular manufacturing.^[2]
• 39% of companies are increasing their recruiting efforts to create a larger data science staff.^[3]
• According to 43% of millennials, a hybrid human-bot customer support channel would be more expensive.^[3]
• Organizations utilizing AI claim lower expenses since 45% of end consumers choose chatbots as their main method of contact for customer support questions.^[3]
• 47% of AI-led businesses said they could optimize sales and marketing, while 32% said they were able to reduce operating costs.^[3]
• When the machine learning powered GNMT-a translation algorithm was used instead of google translate, mistakes were reduced by 60%.^[3]
• Customers are eager to share their data with AI in 62% of cases in order to improve user and business experiences.^[3]
• For performance analysis and reporting, 74% of data scientists and C-level executives use machine learning.^[3]
• When AI is present, 49% of consumers are willing to shop more frequently, while 34% will spend more money.^[3]
• Companies using AI for sales increased their leads by more than 50%, reduced call time by 60-70% and realized cost reductions of 40-60%.^[3]
• AI is being used by executives to eliminate time consuming operations like paperwork (82%), scheduling (79%), and timesheets (78%).^[3]
• In terms of forecasting a patient’s mortality, Google’s AI machine learning system has a 95% accuracy rate.^[3]
• B2B companies that have leveraged AI in sales realized call-time reductions of up to 70% and a 50% increase in leads and appointments.^[3]
• 49% of customers are eager to shop more often and 34% are ready to spend more money when AI is there.^[3]
• According to O’Reilly, 12% of those who belonged to organizations that are just beginning to explore machine learning stated that they relied on external consultants, whereas 73% of those who belonged to the most sophisticated companies relied on their internal data science teams.^[4]
• According to Refinitiv AI/ML Survey, 46% of respondent have deployed ML in multiple areas and it is core to business.^[4]
• In 10 different nations and 14 distinct sectors, 20% of C-level executives say that machine learning is a key component of their businesses.^[4]
• According to Algorithmia, budgets for ML programs are growing most often by 25%, and the banking, manufacturing, and IT industries have seen the largest budget growth this year.^[4]
• Only 4.5% of data scientists or researchers in the US who self identify as such expressly work as machine learning engineers.^[4]
• Machine learning talent companies with substantial expertise in machine learning already employ a lot of job titles specialized to the field, including data scientist (81%), machine learning engineer (39%), and deep learning engineer (20%).^[4]
• According to MemSQL, 74 % of respondents consider ML and AI to be a game changer, indicating it had the potential to transform their job and industry.^[4]
• Deep learning feed forward neural networks recurrent neural networks and convolutional neural networks make about 40% of the yearly value that all analytics approaches have the ability to produce.^[4]
• The accuracy of Azure Machine Learning framework in predicting stock market highs and lows is 62%.^[4]
• In terms of forecasting a patient’s mortality, Google AI’s machine learning system has a 95% accuracy rate.^[4]
• At a CAGR of 39%, the market for machine learning will grow from $8 billion in 2019 to $117 billion by the end of 2027, according to GlobeNewswire.^[4]
• With Kiva’s capabilities, Amazon’s average ‘click to ship’ time reduced by 225% from 60-75 minutes to 15 minutes, according to Forbes.^[4]
• Just 45% of data scientists or researchers who identify themselves as such in the US work solely as machine learning engineers, according to Kaggle.^[5]
• According to Thinkful, a full time data scientist in the United States will earn an average of 12.0 per year in 2021.^[5]
• About 80% of those who have employed machine learning and artificial intelligence claim that doing so has enhanced their revenue.^[5]
• The machine learning market is projected to grow throughout the projection period at a CAGR of 44%, from 1 billion in 2016 to 9 billion by 2022.^[5]
• In identifying breast cancer, Google’s Deep Learning ML machine learning engine has an accuracy of 89%.^[5]
• According to O’Reilly, data scientists make up 81%, machine learning engineers make up 39%, and deep learning engineers make up 20% of all job titles at companies with significant machine learning competence.^[5]
• When used to forecast highs and lows on the stock market, machine learning has a 62% success rate.^[5]
• When it comes to machine learning utilization, North America is in the forefront at 80%, followed by Asia at 37% and Europe at 37%.^[5]
• The market for machine learning was estimated to be worth $8 billion in 2021 and is projected to increase by 39% annually to reach $117 billion by 2027.^[5]
• Between 2012 and 2021, the number of data scientist jobs on LinkedIn increased by more than 65%.^[5]

Also Read

How Useful is Machine Learning

Proponents of machine learning argue that the technology has revolutionized the way we approach problem-solving and decision-making. By utilizing algorithms and data analysis, machine learning can predict outcomes, identify patterns, and provide valuable insights that humans may overlook. This has proven to be incredibly beneficial in fields such as healthcare, where machine learning can analyze vast amounts of patient data to help diagnose illnesses, develop treatment plans, and predict outcomes with impressive accuracy.

Furthermore, in industries like finance, machine learning can analyze market trends, predict stock prices, and detect anomalies in financial transactions to help prevent fraud. This has not only saved companies millions of dollars but has also improved overall security and trust in financial institutions.

In marketing, machine learning is used to personalize customer experiences, tailor product recommendations, and optimize advertising campaigns to target specific demographics. This has led to higher engagement rates, increased conversions, and improved return on investment for companies looking to reach their target audience more effectively.

However, critics of machine learning raise concerns about its potential drawbacks and limitations. One of the main criticisms is the lack of transparency in how machine learning algorithms make decisions. This black box nature of machine learning algorithms can make it difficult for humans to understand and interpret the reasoning behind the predictions made by these systems. This can be concerning, especially in critical areas like healthcare, where decisions made by machine learning algorithms can have life-changing consequences.

Additionally, there is also the issue of biases present in machine learning algorithms. Since machine learning models are trained on historical data, they can inadvertently perpetuate biases that exist within the data, leading to discriminatory outcomes. This has raised questions about the ethics of using machine learning in decision-making processes, particularly in areas like hiring, lending, and criminal justice.

Despite these concerns, it’s clear that machine learning has proven to be a powerful tool in advancing technologies and improving processes across various industries. As with any technology, it is important to recognize the potential benefits and drawbacks of machine learning and work towards addressing any ethical and transparency issues that may arise.

In conclusion, the usefulness of machine learning cannot be denied in our rapidly evolving world. While there are valid concerns about biases and lack of transparency, the potential benefits of machine learning in improving efficiency, accuracy, and innovation cannot be overlooked. It is crucial for us to continue exploring the possibilities of machine learning while also ensuring that ethical considerations are prioritized to create a more responsible and sustainable future.

Reference

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1. wikipedia – https://en.wikipedia.org/wiki/Machine_learning
2. financesonline – https://financesonline.com/machine-learning-statistics/
3. g2 – https://learn.g2.com/machine-learning-statistics
4. aimultiple – https://research.aimultiple.com/ml-stats/
5. eescorporation – https://www.eescorporation.com/machine-learning-statistics/