Machine Learning Statistics 2024 By Programs, Technology, Performance

What Is Machine Learning?

Machine Learning Statistics: Machine learning is the study of teaching machines to learn without explicitly programming them.

This is a form of artificial intelligence that allows computers to improve their performance by analyzing large datasets and patterns.

Algorithms that use machine learning can be taught from examples. They can also discover patterns hidden in data and then make predictions or make decisions based on this knowledge.

These algorithms are used for various applications such as speech recognition and image classification.

Machine learning can be divided into 3 types supervised learning (labeled data), unsupervised learning (unlabelled data), and reinforcement learning.

In supervised training, the machine uses labeled data. Unsupervised learning is based on unlabelled information.

Reinforcement Learning involves teaching a machine how to make decisions using feedback from its environment.

What is the purpose of machine learning?

Machine learning, as fascinating as the title may be, is the subset of artificial intelligence (AI), which does the least amount of learning. It is still one of the most common types of AI. Machine learning software automates and simplifies processes using simple programs.

Machine learning is everywhere in today’s world. It’s used by nearly every industry. utilities contain automatic responses to queries, automated trading in stocks, computer vision, recommendations engines, customer service, and recommendation engines.

Machine learning encompasses the majority of AI projects and software. The machine learning market also represents the largest segment in the AI Market. The market is predicted to grow from 21.5 billion U.S. Dollars to almost 582.4 U.S. Dollars by 2032.

Key Machine Learning Statistics

• The global Machine Learning Operations (MLOps) Market size is poised to cross USD 2.98 Billion in 2024 and is likely to attain a valuation of USD 75.42 Billion by 2033. The Machine Learning Operations (MLOps) industry share is projected to develop at a CAGR of 43.2% from 2024 to 2033.
• 8% of businesses use machine learning, deep learning, data analysis, and natural language processing to make the most of large data sets.
• A significant 82% of businesses are in search of employees who possess machine learning expertise.
• An impressive 73% of company leaders think that machine learning has the potential to double their employees’ productivity.
• Any business will prioritize security. Around 25% of I.T.
• The AI hardware market is expected to reach $87.68 Billion by 2026.
• COVID has caused a 12% reduction in A.I. chip-making companies.
• The American deep-learning market is expected to reach $80 million in value by 2025.
• 91.5% of the top companies invest in artificial intelligence.
• Between 2022 and 2029, the worldwide machine-learning sector is expected to experience a compound annual growth rate of 38.8%.
• Employment opportunities for machine learning engineers are predicted to expand at a 22% rate globally from 2020 to 2030.
• 75% of artificial intelligence projects are overseen personally by senior executives.
• Over half (56.4%) of mobile users interact with AI-driven voice assistants.
• Most marketers (61%) identify machine learning and AI as the most critical aspects of their data strategies.
• Tesla’s autonomous vehicles have logged over 188 billion miles in the fourth quarter of 2019
• Voice assistance usage increased by 5% when used multiple times over six months.

Detailed Machine Learning Statistics

Machine Learning, Deep Learning, and Natural Language Processing are used by 48% of businesses to effectively use large data sets.

• One in three I.T. Machine learning is now an important part of data analysis in the world of business.
• Every second, modern businesses generate terabytes worth of data.
• Many software programs now include machine learning algorithms.
• This helps individuals better understand the importance of certain data to a company.

For any business, security is a top priority. About 25 percent of I.T. Specialists Want Ml (Machine Learning) to Be Used for This Resolution

• Further, 16% believe that machine learning is efficient and effective for marketing & sales.
• Although the growth of smart devices is good for business (computers smartphones tablets etc.), security issues are becoming a higher primacy because hackers continuously seek new ways to infiltrate new tech. Although the growth of smart devices is great for businesses, security concerns are increasing due to hackers constantly looking for new ways to penetrate new technology.
• Machine learning algorithms appear to be an excellent solution to the increasing concerns about security.
• Marketing & sales are also places where companies use ML algorithms. They do this for targeted marketing which has so far proven more efficient than blanket advertising.

Machine Learning Doesn’t Show Much in the Way of Cost Reductions but it Shows Revenue Increases. 80% of respondents to a survey reported that Artificial Intelligence is Increasing Revenues. Machine Learning (ML), which boosts revenues, is a growing trend.

• It would seem that ML is increasing revenue, not decreasing costs.
• Although this isn’t a bad thing in itself, many I.T. Professionals and businesspeople are confused that these two factors of data are not happening simultaneously.
• The increase in revenue may compensate for the lack of cost reductions.
• It’s great to know that 80% say that ML is helping them increase their revenue.

The A.I. The Hardware Market Value is Expected to Climb Up to $87.68 billion by 2026.

• This is expected because of the projected CAGR (compound annual growth rate) of 37.60% between 2019 and 2026.
• Many people think A.I. is only software, but the hardware components are also just as important.
• The A.I. of today is heavily dependent on computing power. For this reason, AI hardware is expected to become more important in the future.
• Most AI programs are currently used in chatbots, factory machines, and other applications.

Machine Learning Market Size

The worldwide market of machine learning is expected to grow at a CAGR of roughly 39.1% over the next ten years and will reach US$ 582.4 Bn in 2032, from US$ 21.5 Bn in 2022, according to a new Market.us study.

Growth Rate

The compound annual growth rate of the machine learning market is forecast to be 39.1% between 2022 and 2032.

Driving Forces for Machine Learning Growth

Big Data, Advances in computing power, Open source software, Increased automation, Business applications, and Research breakthroughs.

Key Players Driving Machine Learning

The key companies in the machine learning sphere include International Business Machines, Microsoft, Sap, Sas Institute, Amazon Web Services, Bigml, and Google.

(Source: Market.us)

Machine Learning Adoption by Companies

A recent report on machine learning shows that the adoption of ML has reached new levels. The adoption of ML has increased at a rapid pace as companies try to use the technology to stay ahead of their competition.

The financial sector has been one of the biggest adopters and now uses AI software. These tools use machine learning to analyze data and find insights.

Machine learning capabilities such as risk management, performance analyses, reporting, and automation are driving factors for development. Here are some statistics about ML adoption.

• Half of the respondents said that their company had adopted AI in at least one area.
• 1/3 of IT leaders intend to use machine learning for business analytics.
• 25% of IT leaders plan to use ML for security.
• According to machine learning statistics for 2019, 16% of IT executives are interested in using machine learning for marketing and sales.
• The top three business functions adopting AI in 2020 are the same as those that were adopted in 2019: Sales and Marketing, Service Operations, and Product/Service Development.
• AI adoption is often associated with increased revenue. Cost reductions are not always reported. As an example, 80% of respondents said that AI helped increase revenue.
• AI and machine learning have the potential to boost global GDP from now until 2030 by 14%.
• Scaling up (43%) and modifying the ML model (41%) are the biggest challenges to machine learning adoption.

(Source: McKinsey, Statista, WSJ)

Machine Learning in Voice Assistants

ML is a subset of “deep learning” which is based on machine learning. This is the technology behind the voice assistants Siri, Echo, and Google Assistant.

The popularity of voice assistants has increased among consumers following the explosion in mobile technology. Check out the statistics below to see how this has developed.

• Around 3.25 billion people worldwide use voice-activated assistants and search engines, which is almost half the population of the planet.
• Global voice assistant usage during COVID-19 rose by 7%
• People began to use voice assistants more often because of the pandemic. The number of people who use it daily rose from 20% in December 2019 to January 2020 to 25% in March and April 2020.
• In 2020, voice assistants will be used by 128 million Americans, an increase from 115.2 million in 2019.
• Voicebot.ai found that 80.5% of under-30 consumers used a voice assistant for their smartphones, compared to only 60.5% in the older age groups.
• Voicebot.ai reports that 74.7% (of consumers aged 30-44) use voice assistants for their smartphones. 68.8% (of consumers aged 45-60%) do the same.
• By 2023, it is expected that 8 billion people worldwide will use voice assistants.
• The estimated value of the global market for natural language processing will be $43 billion.

(Source: Voicebot.ai, eMarketer, Review42, Statista)

Top Countries Considered Early Adopters of Machine Learning Methods and Tools

• Israel
• Netherlands
• United States
• UK and Northern Ireland
• Germany
• Australia
• France
• China
• Taiwan
• Greece

Machine Learning Benefits Statistics

• 38% of companies reduced their business costs by utilizing machine learning.
• Machine learning is helping 34% of companies improve their customer service.
• Machine learning helped 27% of companies identify and reduce fraud in their operations.
• Netflix saved an estimated $1 billion through machine learning.
• Google Translation mistakes are reduced by 60% when machine learning is used.
• When machine learning was used during a pandemic, it was 92% correct in predicting the mortality of COVID-19 patients.
• AI can help to avoid 81% or more of cybercrime.
• 65% of business owners say that machine learning helps them improve decisions.
• Chatbots are chosen by 45% as the main provider of customer service over real agents.
• AI is anticipated to increase business profits by 38% between 2035 and 2035. This will yield an additional $14 trillion for the same businesses.

The Machine Learning For Healthcare

• In the healthcare sector machine learning is used in early identification of potential pandemics, ML-based medical diagnosis, and tracking incidence of the disease and imaging diagnosis.
• The global market of machine learning in the healthcare sector was valued at 11 billion by Statista.
• In the healthcare sector clinical trials segment was the leading segment in which machine learning is used.
• Regionally North America dominated the market of machine learning in healthcare as well as globally because of advanced technology developed by North America.

Machine Learning For Banking

According to McKinsey, AI and its related technologies will have a seismic impact on all aspects of the insurance industry, from distribution to underwriting and pricing to claims.

The biggest ML and AI potential in the sector will be observed in areas such as personalized financial planning, fraud detection and anti-money laundering, and process automation.

(Source: PwC)

• In 2022, the global AI market in BFSI is estimated to be $3.23 billion.
• The projected global AI market in BFSI is $15,32 billion by 2028. This market will grow at a 29.6% CAGR during the forecast period of 2022-2028.
• 80% of banks have a high awareness of the benefits of AI and machine learning. 75% of respondents from banks with assets over $100 billion are currently implementing AI strategy.
• 60% of respondents in the financial services sector say they have at least one AI capability.
• Automation of middle-office tasks using ML and AI could save North American banks up to $70 billion in 2025.
• AI platform revenues in insurance are expected to grow 23% between 2019 and 2024, to $3.4 billion.
• The “black box” problem is one of the major roadblocks that prevent banks from successfully implementing their ML/AI strategies.
• 76 % of respondents of Statista’s report consider applying AI and ML technology in stock market workflows.

(Source: Business Insider, McKinsey, Insider Intelligence, Deloitte, GlobalData, Statista)

Machine Learning Skills Demand and Employment Statistics

While AI and ML are becoming mainstream, the advances in AI and ML are being reduced by the lack of employees with mandatory skills. According to Statista, 82% of organizations need machine learning skills and only 12% of enterprises state the supply of ML skills is at an adequate level.

Jobs Using Machine Learning

In 2021, there were 33,500 computer and information science jobs. This number is expected to grow to reach 40,600 jobs by 2031.

(Source: Bureau of Labour Statistics)

Employment Predictions from 2022 Through 2032

Jobs in computer and information science are expected to grow by 21% from now until 2031. Computer operations jobs will grow by 15%. This is a rapid growth compared to the total expected growth of 5% for all occupations in the same timeframe.

In addition, there are an average of 3,300 positions available each year in the field of computer and information science due to the need for workers to replace those who leave the workforce or change careers.

Average Compensation of Jobs Using Machine Learning Skills

In 2021, the median annual salary for computer and information researchers was $131.490, with the lowest 10 percent earning less than $75,210 and the highest 10 percent earning more than $28,000.

Jobs Using Machine Learning

In 2021, there were 33,500 computer and information science jobs. This number is expected to grow to reach 40,600 jobs by 2031.

Employment Predictions from 2022 Through 2032

Jobs in computer and information science are expected to grow by 21% from now until 2031. Computer operations jobs will grow by 15%. This is a rapid growth compared to the total expected growth of 5% for all occupations in the same timeframe.

In addition, there are an average of 3,300 positions available each year in the field of computer and information science due to the need for workers to replace those who leave the workforce or change careers.

Average Compensation of Jobs Using Machine Learning Skills

In 2021, the median annual salary for computer and information researchers was $131.490, with the lowest 10 percent earning less than $75,210 and the highest 10 percent earning more than $28,000.

(Source: Bureau of Labour Statistics)

Machine Learning Technology Trends Statistics

Foundation Models

The large language models have gained popularity and are likely to remain popular for the foreseeable future. The foundation models are artificial intelligence (AI) tools that have been trained using vast amounts of data.

Engineers are trying to reach a higher level of understanding by teaching machines not only to search for patterns but to also acquire knowledge. The foundation models are extremely helpful for content generation, summarization, translation, and coding, as well as customer support. GPT-3, MidJourney, and other foundation models are well-known.

The foundation models are also able to scale quickly and can work with data that has never been seen before. This is why they are so amazing at generating. NVIDIA, Open AI, and other leading providers are the main players in this market.

Multimodal Machine Learning

When a model is used to perform tasks such as computer vision, natural language processing, or other tasks that require interaction between the model, and the real world, it often relies on only one type of information, whether images or text.

In reality, however, we experience the world through a variety of senses, including smell, hearing, and feeling textures.

Multimodal machine learning suggests that we can use the fact that our world is experienced in many different ways (called modality) to create better models.

The AI term “multimodal”, describes the building of ML models which can perceive an event in different modalities, at once, just as humans do.

Combining different types of data and using it in training can help you build an MML. Matching images with audio or text labels can make it easier to identify them.

Multimodal machine learning is a relatively new field, that will continue to develop and advance in 2023. However, many people believe it is the key to general AI.

Transformers

The Transformers is an artificial intelligence architecture that performs transduction or transformation on an input data sequence using encoders and decoders and transforms the sequence into another.

Transformers are used in many foundation models. We wanted to highlight them separately because they are used in many other applications. Transformers are reportedly sweeping the AI world.

The Transformers, also known as the Seq2Seq model, are used widely in natural language processing and translation.

Transformers are better than artificial neural networks because they can analyze words in sequences rather than as individual words.

A transformer model can assign weights to each word of a sequence, rather than translating the sentence word-by-word.

The model then transforms the sentence into another language, taking into account the weights assigned. Hugging Face, Amazon Comprehend, and other solutions can be used to build transformer pipelines.

Embedded Machine Learning Statistics

TinyML (or embedded machine learning) is a subfield in machine learning that allows machine learning technologies to run on different devices.

TinyML can be found in home appliances, laptops, smartphones, and smart home systems. As Lian Jye Su, AI & ML Principal Analyst at ABI Research

One of the main drivers of the industry is the growing popularity of embedded machine-learning systems. Moore’s Law, which predicted the rise in computing power, allowed us to double the number of transistors in a chipset every two years ten years ago.

In the last few decades, however, we have seen an increase of 40-60% per year. This trend is expected to continue in the coming years.

Embedded systems are becoming increasingly important as IoT and robotics technologies proliferate. Tiny ML presents its unique challenges which will not be solved until 2023. It requires maximum efficiency and optimization while saving resources.

Low-code and no-code Solutions

Machine learning and artificial intelligence have penetrated every field, from banking to marketing. Managers often consider that making ML solutions simple to use for non-technical employees is a critical factor in maintaining the efficiency of an entire organization.

It’s easier to choose apps that don’t require any coding knowledge or skills. No-code solutions can solve other problems as well.

Gartner found that demand for high-quality solutions is greater than what IT can deliver. “It grows at least five times faster than Its capacity to deliver”.

Low-code and no-code solutions can bridge the gap and meet demand. Low-code solutions allow tech teams to test and develop their hypotheses faster.

This reduces time-to-delivery and development costs. Ten years ago it took a team to launch a new website or build an app.

Today, one person can accomplish the same task. No-code and Low-code techniques are used by 82% of companies to create and maintain apps.

While low-code or no-code software solutions have been available for some time, they are still inferior to regular development. The AI market will reward startups that can improve the current situation.

Cloud computing is a key technology behind many of the latest innovations. Statistics show that 60% of corporate data in the world is stored in the cloud.

This number is expected to increase. We will see an increase in investment for cloud security and resilience by 2023 to meet the growing demands of the ML Industry.

Recent Developments

Acquisitions and Mergers:

• Google’s acquisition of DataRobot: In January 2024, Google acquired DataRobot, a leading provider of automated machine learning (AutoML) solutions, for an estimated $5 billion. This acquisition is aimed at bolstering Google’s AI capabilities and expanding its presence in the machine-learning market.

New Product Launches:

• Microsoft’s Launch of Azure ML Studio 2.0: Microsoft unveiled Azure ML Studio 2.0 in March 2024, offering enhanced features for data scientists and developers to build, deploy, and manage machine learning models in the cloud. The updated platform includes advanced analytics tools and integrations with popular frameworks like TensorFlow and PyTorch.

Funding Rounds:

• OpenAI Secures $1.2 Billion in Series E Funding: In April 2024, OpenAI, a leading artificial intelligence research organization, raised $1.2 billion in Series E funding led by prominent investors. The funding will support the development of advanced machine learning models and AI technologies.

Partnerships and Collaborations:

• NVIDIA Partners with Siemens for AI-Driven Manufacturing: NVIDIA announced a strategic partnership with Siemens in February 2024 to integrate NVIDIA’s AI technologies, including machine learning, into Siemens’ industrial automation solutions. The collaboration aims to enhance manufacturing processes with AI-driven insights and predictive analytics.

Research and Development Initiatives:

• Facebook AI Research’s Breakthrough in Self-Supervised Learning: In March 2024, Facebook AI Research (FAIR) published a paper detailing a breakthrough in self-supervised learning techniques. The research demonstrates significant improvements in machine learning models’ ability to learn from unlabeled data, paving the way for more efficient and scalable AI systems.

Industry Collaborations for Ethical AI:

• IBM Joins Partnership on AI: In February 2024, IBM became a member of the Partnership on AI, a multi-stakeholder initiative focused on advancing the understanding and development of ethical AI technologies. IBM’s participation underscores its commitment to responsible AI practices and collaboration with industry peers.

Expansion into Emerging Markets:

• Tencent’s Investment in AI Startups in Southeast Asia: Tencent announced plans to invest in AI startups in Southeast Asia in January 2024, as part of its strategy to expand its presence in the region’s burgeoning machine-learning market. The investments aim to foster innovation and drive the adoption of AI technologies in diverse industries.

Regulatory Developments:

• EU Proposes AI Regulation Framework: In March 2024, the European Union unveiled a proposed regulatory framework for AI, including machine learning technologies, aimed at promoting transparency, accountability, and ethical use of AI systems. The proposed regulations could have significant implications for companies operating in the machine learning sector within the EU.

Conclusion

Machine Learning Statistics – Everyone agrees that machine learning and deep-learning applications will define the next decade.

Few people can take advantage of its potential. Machine learning statistics show that it is still in its testing phase. Professionals need to grow their expertise for its sustainability.

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