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DUBLIN, Dec. 22, 2021 /PRNewswire/ -- The "Artificial Intelligence In Remote Patient Monitoring Market Research Report by Product, by Solution, by Technology, by Application, by Region - Global Forecast to 2026 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence In Remote Patient Monitoring Market size was estimated at USD 712.67 million in 2020 and expected to reach USD 892.99 million in 2021, at a CAGR 25.63% to reach USD 2,803.19 million by 2026.

Market Statistics:

The report provides market sizing and forecast across five major currencies - USD, EUR GBP, JPY, and AUD. It helps organization leaders make better decisions when currency exchange data is readily available. In this report, the years 2018 and 2019 are considered historical years, 2020 as the base year, 2021 as the estimated year, and years from 2022 to 2026 are considered the forecast period.

Competitive Strategic Window:

The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence In Remote Patient Monitoring Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

Company Usability Profiles:

The report profoundly explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence In Remote Patient Monitoring Market, including 100 Plus, AiCure, Binah.ai, Biofourmis, Cardiomo, ChroniSense Medical, ContinUse Biometrics (Cu-Bx), Current Health, Ejenta, Eko, Engagely.ai, Feebris, GYANT, iHealth, Medical Device + Diagnostic Industry (MD+DI), Medopad, Myia, Neoteryx, LLC, Neteera, Tech Vedika, ten3T Healthcare, and Vitls.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on the market offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:1. What is the market size and forecast of the Global Artificial Intelligence In Remote Patient Monitoring Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Artificial Intelligence In Remote Patient Monitoring Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Artificial Intelligence In Remote Patient Monitoring Market?4. What is the competitive strategic window for opportunities in the Global Artificial Intelligence In Remote Patient Monitoring Market?5. What are the technology trends and regulatory frameworks in the Global Artificial Intelligence In Remote Patient Monitoring Market?6. What is the market share of the leading vendors in the Global Artificial Intelligence In Remote Patient Monitoring Market?7. What modes and strategic moves are considered suitable for entering the Global Artificial Intelligence In Remote Patient Monitoring Market?

Key Topics Covered:

1. Preface

2. Research Methodology

3. Executive Summary

4. Market Overview4.1. Introduction4.2. Cumulative Impact of COVID-19

5. Market Dynamics5.1. Introduction5.2. Drivers5.2.1. ICT infrastructure development in developing countries5.2.2. Rise in adoption of AI in remote patient monitoring due to real time monitoring and improved patient engagement5.2.3. Growth in demand due to optimizing management and lower human errors5.3. Restraints5.3.1. Lack of awareness in remote areas5.3.2. Expensive as compared to traditional facilities5.4. Opportunities5.4.1. Rapid digitalization and extensive use of social media of consumer5.4.2. Shift in trend towards wearable technology5.5. Challenges5.5.1. Increasing concern related to cybersecurity and privacy

6. Artificial Intelligence In Remote Patient Monitoring Market, by Product6.1. Introduction6.2. Special Monitors6.2.1. Anaesthesia Monitors6.2.2. Blood Glucose Monitor6.2.3. Cardiac Rhythm Monitor6.2.4. Fetal Heart Rate Monitor6.2.5. Multi-Parameter Monitors6.2.6. Prothrombin Monitors6.2.7. Respiratory Monitor6.3. Vital Monitors6.3.1. Blood Pressure Monitor6.3.2. Brain Monitor6.3.3. Heart Rate Monitor6.3.4. Pulse Oximeter6.3.5. Respiratory Monitor6.3.6. Temperature Monitor

7. Artificial Intelligence In Remote Patient Monitoring Market, by Solution7.1. Introduction7.2. Hardware7.3. Services7.4. Software

8. Artificial Intelligence In Remote Patient Monitoring Market, by Technology8.1. Introduction8.2. Machine Learning (ML)8.3. Natural Language Processing (NLP)8.4. Querying Method (QM)8.5. Speech Recognition (SR)

9. Artificial Intelligence In Remote Patient Monitoring Market, by Application9.1. Introduction9.2. Cancer9.3. Cardiovascular Diseases9.4. Dehydration9.5. Diabetes9.6. Infections9.7. Respiratory Issues9.8. Sleep Disorder9.9. Viral Infection9.10. Weight Management & Fitness Monitoring

10. Americas Artificial Intelligence In Remote Patient Monitoring Market10.1. Introduction10.2. Argentina10.3. Brazil10.4. Canada10.5. Mexico10.6. United States

11. Asia-Pacific Artificial Intelligence In Remote Patient Monitoring Market11.1. Introduction11.2. Australia11.3. China11.4. India11.5. Indonesia11.6. Japan11.7. Malaysia11.8. Philippines11.9. Singapore11.10. South Korea11.11. Taiwan11.12. Thailand

12. Europe, Middle East & Africa Artificial Intelligence In Remote Patient Monitoring Market12.1. Introduction12.2. France12.3. Germany12.4. Italy12.5. Netherlands12.6. Qatar12.7. Russia12.8. Saudi Arabia12.9. South Africa12.10. Spain12.11. United Arab Emirates12.12. United Kingdom

13. Competitive Landscape13.1. FPNV Positioning Matrix13.1.1. Quadrants13.1.2. Business Strategy13.1.3. Product Satisfaction13.2. Market Ranking Analysis13.3. Market Share Analysis, By Key Player13.4. Competitive Scenario13.4.1. Merger & Acquisition13.4.2. Agreement, Collaboration, & Partnership13.4.3. New Product Launch & Enhancement13.4.4. Investment & Funding13.4.5. Award, Recognition, & Expansion

14. Company Usability Profiles14.1. 100 Plus14.2. AiCure14.3. Binah.ai14.4. Biofourmis14.5. Cardiomo14.6. ChroniSense Medical14.7. ContinUse Biometrics (Cu-Bx)14.8. Current Health14.9. Ejenta14.10. Eko14.11. Engagely.ai14.12. Feebris14.13. GYANT14.14. iHealth14.15. Medical Device + Diagnostic Industry (MD+DI)14.16. Medopad14.17. Myia14.18. Neoteryx, LLC14.19. Neteera14.20. Tech Vedika14.21. ten3T Healthcare14.22. Vitls

15. Appendix

For more information about this report visit https://www.researchandmarkets.com/r/7x6wlp

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Insights on the Artificial Intelligence in Remote Patient Monitoring Global Market to 2026 - Featuring 100 Plus, AiCure and Cardiomo Among Others -...

Xiaomi has unveiled MIUI 13 which it plans to unleash on the world in the first quarter of the new year. The firm said that the operating system will be expanded beyond smartphones and tablets to Artificial Intelligence of Things (AIoT) devices such as smart watches, speakers, and TVs. The firm has also improved its software so that it operates better under heavy usage.

According to the company, MIUI 13 improves core functions, increasing the systems fluidity by a whopping 52%. The core apps have also been optimised so they run better while the system is getting bogged down by third-party apps. Xiaomi has also developed technologies called Atomized Memory and Liquid Storage which reduce deterioration by over 5% over a 36-month period; this should help you hold onto devices for longer.

To make MIUI more interoperable with smart devices, the new update will introduce the beta of Mi Smart Hub. Commenting on the new tool, Xiaomi said:

As of Q3 2021, the number of connected devices on Xiaomis IoT platform exceeds 400 million. While leading the industry with its smart hardware portfolio, MIUI 13 will introduce the beta of Mi Smart Hub, which will help realize a more connected experience between smart devices. With Mi Smart Hub, users can find nearby devices and with a simple gesture to seamlessly share and access content such as music, display, even apps across multiple devices.

Finally, MIUI 13 brings new personalisation options through new widgets, dynamic wallpapers, and more. The global version of MIUI 13 will be delivered over-the-air beginning in Q1 2022. The first devices to get the update will be the Mi 11, Mi 11 Ultra, Mi 11i, Mi 11X Pro, Mi 11X, Xiaomi Pad 5, Redmi 10, Redmi 10 Prime, Xiaomi 11 Lite 5G NE, Xiaomi 11 Lite NE, Redmi Note 8 (2021), Xiaomi 11T Pro, Xiaomi 11T, Redmi Note 10 Pro, Redmi Note 10 Pro Max, Redmi Note 10, Mi 11 Lite 5G, Mi 11 Lite, and Redmi Note 10 JE.

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Xiaomi debuts MIUI 13 with support for the Artificial Intelligence of Things - Neowin

I. What is AI?

For many, the term AI still evokes the image of either a Terminator-type robot or a disembodied, talking computer many times smarter than humans. And more often than not, the human race invariably needs to be rescued from their clutches, preferably by a bunch of easy-on-the-eyes Hollywood stars. Fortunately, the reality is considerably less dramatic and more benign. A conscious computer or super-intelligent robot is what experts refer to as Artificial General Intelligence or Singularity and we are still about 40 to 50 years away from that phenomenon.

However, narrow AI is already in our midst in our smart phones talking to us (Siri, Cortana), giving us recommendations (Amazon and Netflix), giving financial advice (Schwabs Intelligent Portfolio) and winning game shows (IBMs Watson). The common thread among all these different activities is the fact that they are replicating what a reasonably smart human being can do sensing, reasoning and acting. Put simply, AI is the ability of machines to perform functions similar to that of a human mind. It is a sub-field of computer science and is aimed at developing a set of computational technologies that are capable of doing things that are done by people.

There is no doubt that AI could be harnessed for the benefit of humanity from healthcare to climate change and humanitarian crises. But there are many risks and challenges that need to be considered and mitigated before embracing it wholly.II. Promise

Globally, AI is being adopted across sectors IT/ITES, fintech, transportation, manufacturing, retail services, healthcare, education, agriculture, law and order but its pace and impact vary widely.

The AI start-up ecosystem in India has included a few truly innovative experiments. For instance, GreyOrange designs and develops warehouse automation and technology solutions and offers products like Butler, a fleet of mobile robots for moving materials in the warehouse more efficiently, Sorter, a fully automated sortation system to sort and divert outbound packets and GreyMatter, a software platform for end-to-end intelligent order fulfilment.

Similarly, NetraDyne is a machine learning and deep learning company that focuses on computer vision and its applications to automotive and unmanned aerial systems navigation and collision avoidance. It also works on automated analysis of visual data collected by drones for verticals ranging from agriculture to site inspections.

Perfint Healthcare, a medical device technology company developing diagnostic equipment for the oncology space, has developed products like Robio EX (CT & PET-CT guided robotic positioning system), Robio EZ (robotic, mobile stand-alone system with 5 DOF for needle placement during CT Scan) and Maxio (image-guided, physician controlled stereotactic accessory device to a CT system).

Bengaluru-based start-up CropIn, uses AI to maximise per-acre value in agriculture. With its smartfarm solution it is possible to geo-tag plots of farm-land to find the actual plot area. It also helps in remote sensing and weather advisory and scheduling and monitoring farm activities for complete traceability.

The police in Punjab and Uttar Pradesh are using facial recognition systems with options like face search and text search. PAIS has a database with more than 100,000 records of criminals housed in jails across Punjab. Trinetra, a product of Gurgaon-based start-up Aqu that the UP Police is now using, has a database has approximately 5 lakh criminals.III. Challenges

Scarcity of big data: The most powerful AI machines are the ones that are trained on supervised learning. This training requires labelled data data that is organised to make it ingestible for machines to learn. However, the availability of well labelled, feature-rich local data sets is extremely limited in India. A few government bodies make some data sets available but they are limited in number and scope.

Lack of clean data: For data to be used to train AI, it needs to be recorded in consistent, machine-readable formats for accuracy and to ensure that it does not present the algorithms with unintended biases. This is a particularly big problem in India as a lot of its data is not digitised or is in unstructured format.

Data localisation: The act of storing data on any device that is physically present within the borders of a specific country where the data was generated is known as data localisation. Free flow of digital data, especially data which could impact government operations or operations in a region, is restricted by some governments for security concerns. However, some experts oppose the move as it is seen as hindering the flexibility of the internet and adding to the cost for global companies who have to maintain multiple local data centres.

Limited Technical Capacity: AI algorithms are usually very complex, often requiring thousands of calculations sometimes even more computed every second. With the development of cloud and distributed processing over the past decade, it became possible to process big data algorithms, ushering in the current age of AI-powered data analytics. However, as demand for more powerful processors increases, bottlenecks will start emerging, making it difficult for enterprises to adopt the technology.

Impact on jobs: The rapid advance in AI technology has sparked concerns about how it would impact employment. There is fear that as AI improves, it could supplant workers creating a pool of unemployable humans who cannot compete economically with machines. While there is no definitive way to predict the scale of job losses or quantify the new jobs that will be created, various studies have attempted to address this question with varying results. For instance, the study by Frey and Osborne predicted that some functions within 47 per cent of jobs will be automated. A report on the OECD countries put the share of jobs potentially lost to computerisation at nine percent. The World Economic Forums 2018 report, however, predicts that a net of 58 million new jobs would be created due to the disruption caused by AI. Most studies1 consistently predict that the least well-off will suffer the most from automation. But a new study by Brookings, published in 2019, gives a different prediction. While stating that almost all occupations can be impacted by AI, it shows through a comparative textual analysis of text of AI patents and the text of job descriptions that it would affect better paid, white-collar occupations such as market research analysts, sales managers, computer programmers and personal financial advisors more than low paying, hands-on services such as personal care, food preparation or health care.2

IV. Threats

Todays AI suffers from a number of novel unresolved vulnerabilities. These include data poisoning attacks (introducing training data that causes a learning system to make mistakes), adversarial examples (inputs designed to be misclassified by machine learning systems), and the exploitation of flaws in the design of autonomous systems goals. They demonstrate that while AI systems can exceed human performance in many ways, they can also fail in ways that a human never would.

Among the threats to political security, the key one comes from the state. The state can use automated surveillance platforms to suppress dissent.

AI can also mislead and confuse. For example, creation of highly realistic videos showing inflammatory comments by influencers that they never actually made. Automated, hyper-personalised disinformation campaigns can be launched using AI. Individuals can be targeted in swing districts with personalised messages in order to affect their voting behaviour.

In addition to these threats which have a malicious intent, there are threats which are unintentional or system related. Take, for instance, algorithmic bias. Algorithmic bias occurs when a computer system reflects the implicit values of the humans who created it. While generally the blame for bias in AI is put on the training data, the reality is bias can creep in long before the data is collected as well as many other stages of the deep learning process during the framing of the problem, collecting data, and preparing the data. For example, biases creep in during hiring decisions as Amazon found out that its internal recruiting tool was dismissing female candidates because it was trained on historical hiring decisions which favoured males over females.3

V. Towards a Responsible AI

The need for ethics and laws to regulate AI is seen as critical for it to gain the confidence of the public. If AI leads to privacy violations, bias, or malicious use, or if much of the world comes to blame it for exacerbating inequality, the potential of AI would remain unfulfilled. Establishing confidence in its abilities to do good, and at the same time, addressing misuses, will be crucial.

This has prompted many countries to take pro-active steps to frame policies to regulate AI. At the same time, tech giants such as Google, Intel, and Facebook have declared ethical standards they plan to adhere to. India has also woken up to the need to regulate AI and has taken some small steps in that direction.

We need to keep in mind that AI is a tool that can be applied with good or ill intent. Therefore, it is important to think of the ethical implications of AI while designing it. Similarly, we need to find a balance between regulations that protect citizens while also not impeding technological breakthroughs.

References:

1 Automation and Artificial Intelligence: How Machines are Affecting People and Places, Muro, Mark; Maxim, Robert; Whiton, Jacob, Brookings, January 2019; A Future that Works: Automation, Employment and Productivity, McKinsey Global Institute, 2017; Arntz, M., T. Gregory and U. Zierahn (2016), "The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis", OECD Social, Employment and Migration Working Papers, No. 189, OECD Publishing, Paris.

2 What jobs are affected by AI? Better Paid, better educated workers face the worst exposure, Mark Muro, Jacob Whiton and Robert Maxim, Metropolitan Policy Program, Brookings, Nov 2019.

3 This is how AI bias really happens and why its so hard to fix, Karen Hao, MIT Technology Review, Feb 4, 2019.

This article is based on excerpts from the book Artificial Intelligence and India (Oxford India Short Introductions), by Kaushiki Sanyal and Rajesh Chakrabarti, Oxford University Press, 2020.https://www.amazon.in/Artificial-Intelligence-India-Oxford-Introductions-ebook/dp/B08B43M548

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Artificial Intelligence: Promise, Challenges and Threats for India - Governance Now

Bharti Airtel Limited and Tata Consultancy Services have announced the successful testing of innovative use cases from TCS Neural Manufacturing solutions suite on Airtels ultra-fast and low latency 5G network.

Airtel has been allocated 5G trial spectrum by the Department of Telecommunications for the purpose of technology validation. Airtelhas rolled out #5GforBusiness initiative and is partnering with leading technology companies to demonstrate a wide range of enterprise grade use cases using high speed & low latency networks.

Airtel and TCS joined forces to test 5G based use cases from TCSs Neural Manufacturing suite of solutions. These solutions help manufacturers build smart, cognitive factories which mimic resilient and adaptive behaviours as well as enable remote robotic operations in potentially hazardous environments like Mining, Chemical plants and Oil & Gas fields to safeguard human capital. They leverage the ultra-reliable low latency communication, enhanced bandwidth, and high device density characteristics of 5G networks and the combinatorial power of emerging technologies like Artificial Intelligence/Machine Learning, computer vision, industrial robotics and Augmented Reality/Virtual Reality to enable autonomous actions.

TCSsuccessfully tested two use cases on Airtels 5G testbed remote robotics operations, and vision-based quality inspection, demonstrating how TCS Neural Manufacturing solutions and 5G technology can transform industrial operations, and significantly boost quality, productivity and safety. The demonstration was done at Airtels 5G Lab in Manesar (Gurgaon).

Randeep Sekhon, CTO Bharti Airtel,said Airtel is spearheading 5G in India. The 5G ecosystem will open limitless possibilities for enterprises to enhance productivity and serve their customers even better with digitally enabled applications. We are delighted to work with TCS as our strategic technology partner to start testing real life 5G applications of the future. This also offers tremendous learnings across the value chain and lays a solid foundation for future application roadmap.

We believe the future of manufacturing is neural, and have been making sustained investments in research, and innovation, and in building intellectual property. We will continue to build new, differentiated capabilities into TCS Neural Manufacturing suite of solutions, harnessing the power of machine vision, machine intelligence and 5G to reimagine and redefine the way smart factories operate. Our partnership with Airtel to deploy and validate these innovative use cases on their 5G network serves as a proof point of the transformative power of these technologies, saidSusheel Vasudevan, Global Head of Manufacturing & Utilities at TCS.

If you have an interesting article / experience / case study to share, please get in touch with us at [emailprotected]

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Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

While a number of definitions of artificial intelligence (AI) have surfaced over the last few decades, John McCarthy offers the following definition in this 2004 paper(PDF, 106 KB) (link resides outside IBM), " It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable."

However, decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, "Computing Machinery and Intelligence" (PDF, 89.8 KB) (link resides outside of IBM), which was published in 1950. In this paper, Turing, often referred to as the "father of computer science", asks the following question, "Can machines think?" From there, he offers a test, now famously known as the "Turing Test", where a human interrogator would try to distinguish between a computer and human text response. While this test has undergone much scrutiny since its publish, it remains an important part of the history of AI as well as an ongoing concept within philosophy as it utilizes ideas around linguistics.

Stuart Russell and Peter Norvig then proceeded to publish, Artificial Intelligence: A Modern Approach(link resides outside IBM), becoming one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiates computer systems on the basis of rationality and thinking vs. acting:

Human approach:

Ideal approach:

Alan Turings definition would have fallen under the category of systems that act like humans.

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Today, a lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. As noted in Gartners hype cycle (link resides outside IBM), product innovations like, self-driving cars and personal assistants, follow a typical progression of innovation, from overenthusiasm through a period of disillusionment to an eventual understanding of the innovations relevance and role in a market or domain. As Lex Fridman notes here (01:08:15) (link resides outside IBM) in his MIT lecture in 2019, we are at the peak of inflated expectations, approaching the trough of disillusionment.

As conversations emerge around the ethics of AI, we can begin to see the initial glimpses of the trough of disillusionment. To read more on where IBM stands within the conversation around AI ethics, read more here.

Weak AIalso called Narrow AI or Artificial Narrow Intelligence (ANI)is AI trained and focused to perform specific tasks. Weak AI drives most of the AI that surrounds us today. Narrow might be a more accurate descriptor for this type of AI as it is anything but weak; it enables some very robust applications, such as Apple's Siri, Amazon's Alexa, IBM Watson, and autonomous vehicles.

Strong AI is made up of Artificial General Intelligence (AGI) and Artificial Super Intelligence (ASI). Artificial general intelligence (AGI), or general AI, is a theoretical form of AI where a machine would have an intelligence equaled to humans; it would have a self-aware consciousness that has the ability to solve problems, learn, and plan for the future. Artificial Super Intelligence (ASI)also known as superintelligencewould surpass the intelligence and ability of the human brain. While strong AI is still entirely theoretical with no practical examples in use today, that doesn't mean AI researchers aren't also exploring its development. In the meantime, the best examples of ASI might be from science fiction, such as HAL, the superhuman, rogue computer assistant in 2001: A Space Odyssey.

Since deep learning and machine learning tend to be used interchangeably, its worth noting the nuances between the two. As mentioned above, both deep learning and machine learning are sub-fields of artificial intelligence, and deep learning is actually a sub-field of machine learning.

Deep learning is actually comprised of neural networks. Deep in deep learning refers to a neural network comprised of more than three layerswhich would be inclusive of the inputs and the outputcan be considered a deep learning algorithm. This is generally represented using the following diagram:

The way in which deep learning and machine learning differ is in how each algorithm learns. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required and enabling the use of larger data sets. You can think of deep learning as "scalable machine learning" as Lex Fridman noted in same MIT lecture from above. Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesnt necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the hierarchy of features which distinguish different categories of data from one another. Unlike machine learning, it doesn't require human intervention to process data, allowing us to scale machine learning in more interesting ways.

There are numerous, real-world applications of AI systems today. Below are some of the most common examples:

The idea of 'a machine that thinks' dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of artificial intelligence include the following:

IBM has been a leader in advancing AI-driven technologies for enterprises and has pioneered the future of machine learning systems for multiple industries. Based on decades of AI research, years of experience working with organizations of all sizes, and on learnings from over 30,000 IBM Watson engagements, IBM has developed the AI Ladder for successful artificial intelligence deployments:

IBM Watson gives enterprises the AI tools they need to transform their business systems and workflows, while significantly improving automation and efficiency. For more information on how IBM can help you complete your AI journey, explore the IBM portfolio of managed services and solutions

Sign up for an IBMid and create your IBM Cloud account.

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What is Artificial Intelligence (AI)? | IBM