Natural Language Processing Technology Market Latest Trend And Business Attractiveness 2023 To 2030 3M Company, Apple, Amazon Webrvices
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The major and emerging players of the Natural Language Processing Technology Market are closely studied considering their market share, production, sales, revenue growth, gross margin, product portfolio, and other important factors. This will help players familiarize themselves with the movements of their toughest competitors in the Natural Language Processing Technology market. The report is just the right tool that players need to strengthen their position in the Natural Language Processing Technology Market. It helps throughout several stages of company development and releasing novel products into the market. It assures a successful product release to the novice players. Several key regions are captured here such as North America, Europe, Middle East, Africa, Latin America and Asia Pacific along with the position of competitors and pricing structure over there. This Natural Language Processing Technology Market report also informs key participants to market their business effectively to the customers that they aim to reach.
Global Natural Language Processing Technology market is split by Type and by Application. For the period 2017-2030, the growth among segments provide accurate calculations and forecasts for sales by Type and by Application in terms of volume and value. This analysis can help you expand your business by targeting qualified niche markets.
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Profitable players of the Natural Language Processing Technology market are:
3M CompanyAppleAmazon WebrvicesBaiduConvergys CorporationDigital Reasoning SystemsDolbey SystemsFacebookFuji XeroxGoogleHP EnterpriseIBM CorporationInteractionsLexalytics
Product types of the Natural Language Processing Technology industry are:
Natural Language UnderstandingNatural Language Generation
Applications of this report are:
Text RetrievalMachine TranslationInformation Extraction
Essential regions of the Natural Language Processing Technology market are:
– Natural Language Processing Technology North America Market includes (Canada, Mexico, USA)– Natural Language Processing Technology Europe Market includes (Germany, France, Great Britain, Italy, Spain, Russia)– Natural Language Processing Technology Asia-Pacific Market includes (China, Japan, India, South Korea, Australia)– Middle East and Africa (Saudi Arabia, United Arab Emirates, South Africa)– Natural Language Processing Technology South America Market includes (Brazil, Argentina)
Key takeaways from the Natural Language Processing Technology market report:
– Detailed consideration of Natural Language Processing Technology market-particular drivers, Trends, constraints, Restraints, Opportunities, and major micro markets.– Comprehensive valuation of all prospects and threats in the– In-depth study of industry strategies for growth of the market-leading players.– Natural Language Processing Technology market latest innovations and major procedures.– Favourable dip inside Dynamic high-tech and market latest trends remarkable the Market.– Conclusive study about the growth conspiracy of Natural Language Processing Technology market for forthcoming years.
The Natural Language Processing Technology market research report contains the following TOC:
1 Report Overview
1.1 Study Scope
1.2 Market Analysis by Type
1.2.1 Natural Language Processing Technology Market Size Growth Rate: 2017 VS 2023 VS 2030
1.3 Market by Application
1.4 Study Objectives
1.5 Years Considered
2 Global Growth Trends
2.1 Natural Language Processing Technology Market Perspective (2017-2030)
2.2 Natural Language Processing Technology Growth Trends by Regions
2.2.1 Natural Language Processing Technology Market Size by Regions: 2017 VS 2023 VS 2030
2.2.2 Natural Language Processing Technology Historic Market Share by Regions (2017-2023)
2.2.3 Natural Language Processing Technology Forecasted Market Size by Regions (2023-2030)
2.3 Natural Language Processing Technology Industry Dynamic
2.3.1 Natural Language Processing Technology Market Trends
2.3.2 Natural Language Processing Technology Market Drivers
2.3.3 Natural Language Processing Technology Market Challenges
2.3.4 Natural Language Processing Technology Market Restraints
3 Competition Landscape by Key Players
3.1 Global Top Natural Language Processing Technology Players by Revenue
3.1.1 Global Top Natural Language Processing Technology Players by Revenue (2017-2023)
3.1.2 Natural Language Processing Technology Revenue Market Share by Players (2017-2023)
3.2 Natural Language Processing Technology Market Share by Company Type (Tier 1, Tier 2 and Tier 3)
3.3 Players Covered: Ranking by Natural Language Processing Technology Revenue
4 Natural Language Processing Technology Breakdown Data by Provider
4.1 Natural Language Processing Technology Historic Market Size by Provider (2017-2023)
4.2 Natural Language Processing Technology Forecasted Market Size by Provider (2023-2030)
5 Natural Language Processing Technology Breakdown Data by End User
5.1 Natural Language Processing Technology Historic Market Size by End User (2017-2023)
5.2 Natural Language Processing Technology Forecasted Market Size by End User (2023-2030)
6 North America
7 Europe
8 Asia-Pacific
9 Latin America
10 Middle East and Africa
11 Key Players Profiles
12 Analyst's Viewpoints/Conclusions
13 Appendix....
FAQs: –
1. What are the sales, production, consumption, imports, and exports of the global market (North America, Europe, Asia-Pacific, South America, Middle East, and Africa)?2. Who are the major manufacturers who dominate the world market?3. What are their current capacity, production, sales, pricing, cost, gross, and revenue operating levels?4. What are the market's risks and opportunities?
As mentioned by the group of researchers and industry analysts, the report on the global market delivers some measurable insights related to Natural Language Processing Technology market. Furthermore, with the support of several business-driven strategies, the market report elaborates the shifting industrial scenarios. Crucial segments are ranked and segregated based on their industry shares in the global market. Moreover, the global Natural Language Processing Technology market research study briefly summarizes various key competition variables that are critical for the industry to determine possible market conditions at the global and regional level. The market report has been widely exhibited in order to deliver specialized industry assessment into the company profiles of the topmost players as well as highly established companies. Therefore, the world Natural Language Processing Technology market has been evaluated as one of the helpful and extraordinary documents for the new entrants and industry players.
Above all, what criteria distinguish success from failure? We identified key parameters based on global market, which include pricing, value, availability, features, financing, upgrades or return policies, and customer service. Most importantly, this market study can assist you in identifying market blind spots. The Natural Language Processing Technology Market research report's estimations and estimates examine the impact of different political, social, and economic factors, as well as current market conditions, on market growth. All of this important information will assist the reader in better understanding the market.
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Preview Release Of ChatGPT Shows Potential Of Artificial Intelligence
Last November, the artificial intelligence (AI) research laboratory OpenAI launched a free prototype of its text-based human conversation simulator called ChatGPT. Over the past four months, more than 100 million users across a wide range of disciplines have been experimenting with the preview version of ChatGPT.
The users are testing the system in fields such as science and journalism research, essay and legal brief writing, software development, math problem solving and language translation, to name just a few. Some of the more creative uses of ChatGPT have included writing limericks, fixing software bugs and songwriting.
ChatGPT is designed to generate natural language responses to questions, provide recommendations and to write copy. It has numerous applications and has the potential to transform the way people interact with technology and each other.
The breakthrough system is based on advanced computer technology known as generative pretrained transformers (GPTs). GPTs are defined as a family of large language models (LLMs) developed by OpenAI that have been trained with large databases of texts.
The 'pre-training' in GPTs refers to the learning process on a large text corpus enabling the language model to predict the next word in a passage. This provides a foundation for the model to perform well without being dependent on task-specific data.
Like the way Google autocompletes web search entries, ChatGPT anticipates the content of inquiries submitted by users. Known as synchronous processing, it interprets questions as they are typed in real-time and generates responses on the fly.
The limitations of ChatGPT, as described by OpenAI, are its tendency to sometimes write, "plausible-sounding but incorrect or nonsensical answers," and its inclination to be "excessively verbose" and overuse certain phrases. The system will also often guess at an answer when asked an ambiguous question as opposed to "asking a clarifying question."
Whatever its drawbacks, ChatGPT represents a significant step forward in AI technology. In December, Ethan Mollick of Harvard Business Review called ChatGPT a tipping point for artificial intelligence, writing, "While versions of GPT have been around for a while, this model has crossed a threshold: It's genuinely useful for a wide range of tasks … While previous generations of the system could technically do these things, the quality of the outputs was much lower than that produced by an average human. The new model is much better, often startlingly so."
The initial release of ChatGPT was based on GPT-3.5. On March 9, OpenAI announced the release of GPT-4, which has been described in a Cornell University research paper as having characteristics which are "strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT."
The authors state that early experiments with GPT-4 show that it exhibits "sparks of artificial general intelligence," that is, it has the ability to simulate thinking and not just answer specific questions, but do things like reason, sense and behave.
There is no doubt that ChatGPT and GPT-4 show how artificial intelligence technologies are increasing productivity. By replacing functions previously carried out by groups of people into a single automated process, tasks can now be completed quickly and accurately by a computer.
While the mass adoption of personal computers beginning in the 1980s had a dramatic impact on productivity, the adaptive and learning features of artificial intelligence tools like GPTs mean productivity will rise exponentially over a much shorter period of time.
For example, today, ChatGPT is a powerful tool for software developers. Using its natural language processing capability, it can model what a developer is trying to accomplish and provide corresponding code snippets. It can also automate repetitive and time consuming tasks without mistakes and inconsistencies typical of direct human coding input.
ChatGPT can quickly and accurately simplify complex computer code and provide comments and documentation that are often more accurate and informative than anything a developer can write.
Artificial intelligence was pioneered in the mid-20th century, with important contributions made by scientists such as Alan Turing, Marvin Minsky and John McCarthy. Turing, a British mathematician and computer scientist, is widely considered a founding father of AI. In 1950, he proposed the Turing Test, a measure of a computer's ability to exhibit intelligent behavior equivalent to that of a human.
Turing's idea was groundbreaking and set the stage for decades of research on machine learning and natural language processing. Turing published a paper in 1950 called "Computing Machinery and Intelligence," in which he discussed the potential for machines to mimic human intelligence through the use of algorithms and programming.
Marvin Minsky, an American cognitive scientist and computer scientist, was a pioneer of AI who, along with John McCarthy, founded the Artificial Intelligence Laboratory at MIT in 1959. Minsky was interested in the idea of machine perception, or the ability of machines to understand and interpret visual and sensory information. McCarthy, who is often credited with coining the term "artificial intelligence" in 1956, was responsible for Lisp, which became a favored programming language for artificial intelligence (AI) research.
ChatGPT can be described as a new generation of artificial intelligence text-based chatbots that were begun in the 1960s. ELIZA, developed by Joseph Weizenbaum in 1966, used pattern matching and substitution methodology to simulate human conversation. It attempted to match scripted responses to a series of psychotherapy questions.
Later in 1988, the chatbot Jabberwacky was created by Rollo Carpenter to simulate entertaining human conversation by expanding pattern matching to include another level of variability to account for the context of questions being asked.
One of the breakthroughs that came in the 1980s was the development of rule-based systems for natural language processing. These systems relied on sets of hand-crafted rules to analyze and generate natural responses, but they were limited in their ability to handle complex and ambiguous language.
In 1995, Artificial Linguistic Internet Computer Entity (ALICE) operated over the internet and added heuristics—the ability to apply shortcuts that humans often use to solve problems—to the previously developed pattern matching methods. In the 1990s, statistical approaches gained popularity in natural language processing, allowing systems to learn from large datasets of text. This led to the development of probabilistic models which were able to handle a wider range of language inputs and generate more accurate outputs.
In the 2000s, with the development of neural network architectures, deep learning emerged as a powerful technique for natural language processing. These models were able to learn and represent complex patterns in language data, leading to significant improvements in language processing accuracy and efficiency.
In 2010, Apple released the first version of Siri as an intelligent personal assistant and learning navigator that uses spoken natural language to perform computer executed duties such as reading text messages, playing music, scheduling events and searching the web for answers to questions. This simulation of audible human conversation was also offered by Google with Google Assistant (2012) and Amazon with Alexa (2014) .
In addition to the software of ChatGPT, the hardware that runs it is a critical factor in the speed and accuracy of its responses as well as the number of queries it can handle simultaneously. The hardware includes a large number of interconnected processors or nodes working together to handle the computational workload.
The platform also includes specialized processors optimized for machine learning and deep learning workloads as well as high-speed networking and storage technologies that enable fast data transfer and retrieval.
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Finally, the advances made in AI, as manifested in ChatGPT, are the product of a collaborative effort among researchers, engineers, and innovators from around the world. The development of AI is truly a global effort, with contributions from individuals and organizations in many different countries.
AI is a field that requires a multidisciplinary approach, bringing together experts from computer science, mathematics, neuroscience, psychology, linguistics, and other related fields. Advances in hardware, software, and data infrastructure have also been made possible by global collaboration and cooperation.
Many countries have made significant investments in AI research and development, and international organizations and conferences such as the Association for Computing Machinery (ACM) provide a platform for researchers and practitioners to share their work and collaborate on new ideas around the world.
However, while ChatGPT brings forward all the accomplishments of computer technology of the past 75 years on a world scale and possesses socially transformative potential, it also remains ensconced within capitalism, its private property for profit system and national state political structures.
The immediate concerns of Wall Street, which has driven up the value of OpenAI to $29 billion following an investment by Microsoft of $10 billion in January, is to ensure that technology oligarchs such as Elon Musk, Sam Altman, Peter Thiel, and Reid Hoffman have a clear path to realizing a return on their financial commitment to the company.
The expectation is that the core technology of ChatGPT will be sold to corporations across all industries as a means of cutting costs and eliminating jobs. In the present economic environment of inflation, rising interest rates and falling share values on Wall Street, this prospect is without question an attractive one for corporate executives, boards of directors and investors.
According to a study by researchers at the University of Pennsylvania, half of the tasks performed by auditors, interpreters and writers can be performed more quickly by AI tools. A report published by McKinsey & Company estimates that 25 percent of work across all occupations could be automated by 2030 and 60 percent of 800 occupations listed by the Bureau of Labor Statistics could have one-third of their work tasks automated in the coming decades.
Meanwhile, as with all other high tech innovation under capitalism, the power of ChatGPT and artificial intelligence are understood to fetch substantial contracts with the Pentagon and defense departments around the globe.
With AI technologies already in use to automate battlefield operations in the imperialist wars of the twenty-first century, including unmanned drone air assaults and targeted assassinations, the power of GPT decision-making is being actively pursued by the US military.
According to an article in Defense One, Lauren Barrett Knausenberger, the chief information officer of the Air Force, said, "I think that there is a lot of benefit to the DOD of being able to find information, of being able to find who's in charge, of being able to rapidly pull together information in general because we do waste a lot of time like with taskers, for instance."
Another report on Vice said that the Pentagon is using ChatGPT to write a news report on February 8 about the launch of a new counter-drone task force. In other words, the Pentagon is leveraging the potential of AI to automate decision-making and to deliver pro-militarist propaganda.
The only way that the progressive content and global power of artificial intelligence technologies such as ChatGPT can be achieved and, as the system's self-definition indicated, "the potential to transform the way we interact with technology and each other" can be realized is through the revolutionary socialist reorganization of society by the working class.
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Will AI Take Over The World? And Other Questions Canadians Are Asking Google About The New Technology
Artificial intelligence and its offshoots can put the Pope in a puffer jacket, write articles about the stock market and provide meeting minutes in mere minutes. In the span of months, using AI for everyday tasks has gained massive steam, thanks in big part to the public release of ChatGPT and the rising popularity of image generators such as Midjourney and DALL-E.
Just this week, 1,300 notable experts signed an open letter asking AI labs to hit pause on training their most powerful systems for six months to give the industry time to create and implement shared safety protocols.
So if like me (a self-titled internet professional whose main professional function is content creation), you've been avoiding jumping on the bandwagon because it all felt a little too much, too fast. I'm here to tell you it's time to giddy up.
In the past year, Google search queries for "AI" or artificial intelligence have jumped. Canadians are curious about the technology and have questions they want answered.
According to Google Trends plug-in Glimpse, Canadians' top questions about AI in the past year have been:
And the fateful seventh most-popular question:
To help you understand this rapidly evolving slice of the internet and the world, here's a nonexhaustive guide to AI from The Globe, with lots of options for further reading on different topics.
What is artificial intelligence?AI refers to the ability of machines to perform tasks that usually require human-level intelligence. It takes in information and uses that to produce more, other or new information. Its development is driven by advances in computing power (how much information a computer can process and work with at once), big data (the mass of information that's now available because our lives are so online and have electronic and accessible records) and machine learning (which uses data and algorithms to imitate how humans learn, getting more accurate over time).
The peril and promise of artificial intelligence
How did we get here?The story of AI often starts with Alan Turing and his 1950 paper Computing Machinery and Intelligence. Turing thought that since humans could use available information and reason to solve problems and decisions, machines could do the same thing. At that time, computers could execute commands but couldn't store them. In 1955 the term "artificial intelligence" was coined. A year later, the Logic Theorist – a program designed to mimic the problem-solving skills of a human – was born.
The following 20 years brought about computers that could store increasingly more information and were also getting faster, cheaper and more accessible. There was ELIZA in the mid 1960s, a rudimentary conversational computer program that somewhat crudely recreated the interaction between a psychotherapist and a patient. The 1980s saw increased funding and the debut of "expert systems," where the decision-making process of a human expert could be mimicked.
In 1997, a computer chess program beat a reigning world champion for the first time. Around the same time, Dr. Cynthia Breazeal was building Kismet, a robot that could recognize and display emotions.
In 2017 there was a breakthrough in natural language processing (a.K.A the way machines process and understand human language) known as "transformers." These transformers used a mechanism called "attention" to hold more than one word in their recent memory, allowing them to serve up more accurate results – for example, using the words around "bat" in a sentence to determine whether the bat you swing or the bat that sleeps during the day is the intended subject, writes Joe Castaldo.
Transformers set the stage for something called Generative Pre-trained Transformer 3, or GPT-3. Released in 2020 by OpenAI, it was "capable of writing prose and essays, and answering questions with a level of precision not seen before," Castaldo writes.
And then in November 2022, Open AI released ChatGPT. Designed with the general public in mind, the tool gave people like you and me a place to play around with the results of decades of AI breakthroughs and advancements. By this time, image-based AI tools such as Midjourney were doing with images what ChatGPT was doing with words. Searching at a blistering pace through its massive repository of stuff, using the words – and sentences, thanks to transformers – of your prompt to tell it what you'd most likely want back.
What are the different AIs available now?It's too soon to answer the "What AI is best" question, but there are some key players for the average person:
ChatGPTBuilt by OpenAI, ChatGPT – the GPT part stands for generative pre-trained transformer – is an AI model trained to process prompts to perform the tasks at hand, such as answering questions or generating code. Check out some of the things its latest iteration, GPT-4, can do, and start playing around yourself by signing up and asking questions.
CohereThis natural language processing company uses large language models to understand language by digesting, essentially, the entirety of the publicly available internet – blogs, digital books, news articles – to write fluently, answer questions, distill a paragraph to its essence and extract important details from a mass of text, Joe Castaldo writes. Cohere's suite of products are aimed at businesses looking to use automation.
MidjourneyThis program uses text descriptions to construct AI-generated images that almost look like photographs. Similar to ChatGPT, artists can use prompts such as "dramatic lighting," "baroque dress," "lobster carapace" and "beautiful woman" to create visuals that the AI software produces by searching through its vast database built and trained by scraping the internet for millions of images with accompanying text that describes them, Gayle MacDonald writes. DALL-E and Stable Diffusion use similar tactics and produce similar results.
VALL-EA new generative AI gizmo from Microsoft, it can convincingly reproduce any human voice based on three seconds of recorded material, Ian Brown writes.
ReplikaAn online chatbot, similar to Character.AI and Chair, Replika is designed to let users create AI-powered virtual companions. Users who had fallen in love with the chatbot were left heartbroken after a recent policy update changed the personality of their companions.
Will AI replace programmers?Dylan Freeman-Grist writes: The irony that this AI boom – and rollout smugness from its true believers – is happening during an almost daily deluge of devastating tech layoffs should not be lost on us. He says that for those in the tech industry who've grown used to the boom times of the past two decades, they might find that, when they least expect it, winter will come for them, too.
Can AI be creative?This is a practical and also an ethical question that strikes at the heart of one of the key AI debates: What makes something human, and what makes a machine non-human? Where do we draw the line?
It's generally understood today that AI cannot generate new ideas on its own, but it can support humans to do so by catalyzing human creativity. Artists are torn between embracing it and trying to break it, Kate Taylor reports.
What can't AI do?A nonexhaustive, not-entirely-serious list:
While one expert has said 90 per cent of content will be generated by AI by 2025, so far this writer is still employed. Some news organizations have begun to use AI for more straightforward tasks: BuzzFeed is reportedly using it to crank out quizzes. Brennan Doherty writes that "generative AI at this point can't match the soul, emotional depth or flexibility of a human writer." He spoke with professional writers who don't see the advancements as a threat for The Globe.
Where is AI used?All over the place. It's used in health care, finance (with varying degrees of success), business, social media (Canva and Adobe recently rolled out a host of AI-powered capabilities to help make content creation easier), self-driven vehicles, factories, chatbots for customer service, chatbots for relationships, food apps, e-commerce, video games, audio text converters, transcription tools, your smart home device, Google, autocorrect, the legal profession, marketing, education, web development, programming, fashion, art, policing and much more.
Will AI take over the world?AI will certainly continue to change the world, but whether it can take over the world is harder to answer.
"The launch of ChatGPT near the end of last year marked an inflection point, when one of the most advanced tools of its kind was made available to the public at no cost," writes Jared Lindzon. He says the question is no longer if AI will change the world, but how, and how quickly?
A lot of people are terrified, Ian Brown writes. "ChatGPT will undoubtedly boost global productivity. It could also vaporize any job that qualifies as intellectual human labour, especially of a middling or rote nature, where originality is less important than dogged thoroughness."
More than just changing who does what work, New York University's quarterly Threat Landscape Report pointed out last August that, "synthetic and otherwise manipulated assets are a weapon for disillusionment and dissuasion more than for persuasion." As it becomes harder to detect the difference between something that's real and something that was generated by a computer or a program, the result could be a much less trusting world.
Brown spoke with Richard Boyd, the president of Tanjo Inc. In North Carolina, who is famous for having built an AI version of his late father. He has no trouble foreseeing a future in which machines not only learn, but improve themselves as they learn, and become even sentiently intelligent. "That's the last thing we'll need to invent," he told Brown. "Because once we invent that, the machines will outstrip us. The only question then will be, will the machines keep us around?"
If you have further questions about artificial intelligence, send them by e-mail to [email protected] and we will try to answer them for you.
