Category Archives: Artificial Intelligence

Since AI has become a popular technology in the current industry, Artificial Intelligence vs. Human Intelligence has led to debates.

Artificial Intelligence has come a long way from being a part of science fiction to reality. Nowadays we are equipped with many intelligent devices, including self-driving vehicles, intelligent virtual aides, chatbots, and surgical robots. Since AI has become a popular technology in the current industry and a part of the everyday life of the common man, Artificial Intelligence vs. Human Intelligence has led to debates.

Artificial Intelligence is a Computer Science Branch that aims at developing intelligent machines that carry out a broad variety of tasks, typically involving human intelligence and expertise. These smart machines are based on experience and historical information, assessing their environment, and conducting appropriate activities.

Human Intelligence refers to the cognitive capacity of human beings that enables us to think, gain from various experiences, comprehend abstract concepts, apply logic and rationality, solve problems, identify patterns, make observations and choices, retain knowledge, and interact with fellow humans. It is supported by abstract emotions such as self-confidence, enthusiasm, and motivation to enable humans to perform complex tasks.

The pace of implementation A doctor could make a diagnosis in around ten minutes, but a million could be made by the AI system simultaneously.

Less partial There are no partial views on decision making.

Operational capacity-They dont foresee an end to their work due to saturation

Accuracy-The specificity of the outcomes clearly enhances

In many activities, artificial intelligence is critical, in particular when it comes to tedious decisions.

Human Intelligence and pace of AI

Computers can handle more data at a faster rate, as opposed to humans. For instance, AI can solve 10 issues in a minute if the human mind can solve a mathematical problem in 5 minutes.

Making Decisions

In decision making, AI is highly analytical as it assesses based on strictly collected data. The judgments of humans, however, can be affected by individual traits that are not based on statistics itself.

Multiple roles

Human knowledge supports the multifunctional mission, as proved by separate and simultaneous functions, while AI can perform fewer tasks only when a machine can learn duties one by one.

Social interacting

As social beings, people can process abstract knowledge, become self-confident and sensitive to others feelings, and can interact much better. On the other hand, AI has not developed its ability to collect valuable social and emotional knowledge.

According to Pew Research Center, Experts say the rise of artificial intelligence will make most people better off over the next decade, but many have concerns about how advances in AI will affect what it means to be human, to be productive, and to exercise free will.

One example among others that they mentioned, Marina Gorbis, executive director of the Institute for the Future, said, Without significant changes in our political economy and data governance regimes, AI is likely to create greater economic inequalities, more surveillance and more programmed and non-human-centric interactions. Every time we program our environments, we end up programming ourselves and our interactions. Humans have to become more standardized, removing serendipity and ambiguity from our interactions. And this ambiguity and complexity is what is the essence of being human.

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Impact of Artificial Intelligence vs Humans and What the Future Holds - Analytics Insight

To drive American leadership in the field of AI into the future, the National Artificial Intelligence Initiative Office has been launched by the White House Office of Science and Technology Policy (OSTP).

The new agency was established under the American Artificial Intelligence Initiative Act of 2020, which was enacted and codified into law to expand many existing AI policies and initiatives throughout the federal government.

The nascentNational Artificial Intelligence Initiative Officeis charged with overseeing and implementing Americas national AI strategy, according to a statement by the White House. It will work to provide federal coordination and collaboration in AI research and policymaking across the government, as well as with private sector, academia and other stakeholders.

The National Artificial Intelligence Initiative Office will be integral to the federal governments AI efforts for many years to come, serving as a central hub for national AI research and policy for the entire U.S. innovation ecosystem,Michael Kratsios, the nations chief technology officer, said in astatementto The Hill. Kratsios is the nations fourth CTO since theoffice was created in 2009under President Barack Obama.

TheAmerican Artificial Intelligence Initiative, which was established in February 2019, identified five central goals for the nations AI direction, including increasing AI research investment, releasing federal AI computing and data resources, setting AI technical standards, building Americas AI workforce and engaging with international allies.

In addition, theSelect Committee on Artificial Intelligence, which was launched by the White House in 2018 to coordinate Federal AI efforts, is being expanded and made permanent, according to the White House. The committee will serve as the senior interagency body responsible for overseeing the National AI Initiative.

Important related efforts in the nations AI strategy were unveiled last August and September when a series of national AI research institutes were announced by the National Science Foundation.

In August of 2020,five new NSF AI instituteswere created at a cost of $100 million to expand AI to a broader range of businesses across the U.S. economy. The initiatives aim to deepen the NSFsartificial intelligenceresearch to expand the nations workforce and drive new possibilities for a wide range of businesses, educational institutions, medicine, banking and other organizations.

Those AI institutes included the NSF AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography,led by a team at the University of Oklahoma; the NSF AI Institute for Foundations of Machine Learning,led by a team at the University of Texas; the NSF AI Institute for Student-AI Teaming,led by a team at the University of Colorado; the NSF AI Institute for Molecular Discovery, Synthetic Strategy, and Manufacturing (or the NSF Molecule Maker Lab),led by a team at the University of Illinois; and the NSF AI Institute for Artificial Intelligence and Fundamental Interactions,led by a team at the Massachusetts Institute of Technology.

Two related AI research institutes are also being created by theU.S. Department of Agricultureover the next five years using $40 million in funding to expand AI research in farming and food processing. They are the USDA-NIFA AI Institute for Next Generation Food Systems,led by a team at the University of California; and the USDA-NIFA AI Institute for Future Agricultural Resilience, Management, and Sustainability at the University of Illinois.

In September,eight additional NSF AI institutes were unveiledin partnership with Amazon, Google, Intel and Accenture. Those companies are contributing toward a $160 million partnership to fund the eight AI Research Institutes scheduled for creation in late 2021 by the National Science Foundation.This effort marked the first time in which direct industry funding for the AI institutes will be received by the NSF, which funded prior AI institutes on its own or with other governmental partners. Companies have participated in the NSF AI research institutes in the past with researchers, materials, content and more, but previously did not make direct monetary contributions.

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Honing In on AI, US Launches National Artificial Intelligence Initiative Office - HPCwire

Its no surprise that artificial intelligence is a key ingredient in the modern tech space. From machine learning to wearables to robotics, the AI across industries is a growing necessity for businesses looking to remain competitive in the long term. Yet there are a few common reasons why businesses often fall short in their AI strategy implementation.

Information for this eWEEK Data Points article was supplied by Dr. Charla Griffy-Brown, Professor of Information Systems and Technology Management, and Associate Dean of Executive and Part-Time Programs at Pepperdine Universitys Graziadio School of Business. Here she discusses five key reasons AI strategies fail and what businesses can do to avoid these pitfalls.

Early work on AI solutions usually involves small subsets of data, which require smaller computing resources. When AI expands into broader production systems, performance can be impacted exponentially. Insufficient attention to performance at scale creates AI systems that appear to work well during testing but quickly become unusable by the business at large.

Solution: Businesses should be accurate in computing requirements for scaling up and test, as often as possible, in a near-production environment.

There are fundamental issues that arise from decisions regarding data architecture. The wrong database can easily render a scaled AI working test system unusable. Furthermore, this is enhanced by data cleansing and preparation problems. For example, manual interventions by humans might be effective in preparing test data, but this typically cannot be scaled.

Solution: Make data architecture decisions based on not just growth but an understanding of the processes required for the data training required to build AI.

One of the biggest challenges facing implementation of new technology is human beings, and AI implementation will only be as strong as the training and support for the staff implementing it. AI solutions must also be developed with a mechanism for ensuring customer facing channels are fully prepared for customer reactions. For example, this could include a temporary spike in phone calls if chatbots arent working properly or a tsunami of emails if a phone answering service isnt getting them where they need to go.

Solution: Realizing that AI requires human work is fundamental to thinking through AI deployment. Businesses will need to implement strategies to address challenges quickly in advance of an AI initiative, including considerations for how it will impact human staff and customers.

Supporting business issues that didnt appear in testing is very challenging to scale. Scaling AI requires production systems to allow for situations not in designs or plans. Over time, new challenges may arise because of changes in the AI system itself. Machine learning is designed to improve itself over time, and usually this improves the accuracy of an algorithm. However, it can also lead to other revelations, such as identifying new patterns of customer behavior or fraud.

Solution: An important part of scaling AI means developing and working through a variety of hypothetical scenarios. Businesses should develop technical and operational contingencies, such as asking how to switch off an AI solution temporarily with minimal disruption.

One of the most important problems in scaling AI for production are the security implications. Cyber risk is an element that has to be considered from all angles when deploying AI. AI introduces new vulnerabilities and represents new risks to established cybersecurity solutions.

Solution: Before deploying AI, companies should develop a risk-based approach to implementation, identifying any points of weakness and reinforcing these appropriately. They may also consider working with a third party to test cybersecurity protections ahead of time to identify points of vulnerability.

If you have a suggestion for an eWEEK Data Points article, email [emailprotected].

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Reasons Why AI Projects Fail, and How to Fix Them - eWeek

HONG KONG and SHANGHAI, Jan. 14, 2021 /PRNewswire/ -- The Ping An Digital Economic Research Center (PADERC), a member of Ping An Insurance (Group) Company of China, Ltd. (HKEx:2318; SSE:601318), has created four new investment strategies for environmental, social and corporate governance (ESG) investing using Ping An's proprietary CN-ESG data for China A-shares, in light of surging demand in China for ESG ratings and data with wider coverage and a better fit for China's market.

Ping An ESG framework aligns with international standards and Chinese regulations

The investment strategies detailed in the report, "Applications of Ping An CN-ESG Data and Framework in Quantitative Investment Strategy", use the proprietary CN-ESG database and scoring framework developed by the Ping An Group. Ping An was the first asset owner in China to sign the United Nations Principles for Responsible Investment. The framework leverages Ping An's expertise in finance and technology and aligns with international standards as well as guidelines from Chinese regulators to incorporate material topics for Chinese companies.

With technologies such as web crawlers, data mining, machine learning, knowledge graphs, natural language processing (NLP) and satellite remote sensing, the CN-ESG system can verify ESG disclosure-based data as well as mine non-disclosure-based data to provide investors with richer multi-dimensional information.

PADERC's report provides an in-depth analysis on the data characteristics, effectiveness, and strategy back-testing results of the CN-ESG database and scoring framework, which covers more than 3,900 listed companies in the China A-share market with five years of historical data (2015-2019). The framework can provide quarterly results that are further adjusted based on news sentiment scores in real-time compared to annual or semi-annual updates from most ESG rating providers.

ESG factors independent of financial factors

PADERC found the Ping An's CN-ESG scores among A-share companies is close to a normal distribution. The factor correlation test results show that scores have notable performance of quality factors. The overall correlation between CN-ESG factors and traditional financial factors is generally low, showing high levels of independence of ESG factors, which indicates these can provide new data and viewpoints for investment decisions.

The results of the factor layered test show that Ping An CN-ESG factors have a relatively strong positive screening effect on the Chinese Securities Index (CSI) 300 and CSI 800 stock pools. The financial window dressing factors constructed by evaluating the quality and authenticity of the company's financial data yielded 11.61% of long-short gains since 2015.

ESG investment strategies that balance excess returns with ESG objectives

Based on CN-ESG data, PADERC constructed four types of ESG investment strategies that use artificial intelligence (AI) to balance excess investment returns and ESG investment targets:

1) Ping An AI-ESG Selected 100 Strategy: This positive screening strategy selects companies with the highest ESG scores. Based on the broader CSI 800 stock pool, it can better leverage additional information from ESG scores. This strategy achieved an annualized excess return of 4.44%. The annual weighted average ESG score quantile of the portfolio is 94.2% among the benchmark stock pool.

2) Ping An AI-ESG Enhancement Strategy: On the basis of ESG scores-based positive screening, PADERC added ESG factors to its Ping An Digital Economic Research Center 500+ No.1 AI Stock Selection Strategy and there was notable excess return. The AI stock selection strategy is based on linear and non-linear algorithms to capture complex market structures to predict the excess return of individual stocks. The Ping An AI-ESG Enhancement Strategy has an annualized excess return of 16.34%, and the annual weighted average ESG score quantile of the portfolio is 78.7% among the benchmark stock pool.

3) CSI 300 ESG Style Index Series:The CSI 300 ESG Growth Index explores the growth value of the CSI 300 stocks, while controlling its tail risks. The CSI 300 ESG Low Volatility Index reinforces the stability features of ESG investment in both the short and long term. The ESG growth index achieved annualized excess returns of 5.67% and the low volatility index achieved 8.61% relative to the benchmark. The annual weighted average ESG score quantile of the portfolios are 75.1% (ESG growth index) and 73.1% (low volatility index) relative to the benchmark stock pool.

Further testing of excess returns shows that the above active management strategies have almost all achieved excess returns in adverse market conditions, including bond crises, annual bear market downturns, Sino-US trade war, and COVID-19, verifying the effectiveness of ESG factors in challenging environments.

4) AI-ESG MAX Strategy: ESG enhancement of mainstream ETFs enables investors to gradually incorporate ESG concepts into their investing process without changing their traditional investing habits. Based on the CSI 300, controlling for sector deviation, this strategy sets tracking errors to 1%, 3% and 5%. Under different tracking error assumptions, the strategy maximizes ESG scores while achieving annualized excess returns of 3.61%, 3.40% and 3.43% respectively against the benchmark. The back-testing results of the strategy over the past five years show good performance, and excess returns were stable. This type of index enhancement strategy based on ESG factors could help drive an increase in the scale of ESG investing.

Building a richer ESG strategy portfolio to meet investors' diverse needs

Ping An's CN-ESG framework will expand to include fixed income ESG data and climate risk-related AI-driven factors. It will enable more diverse investment options, such as ESG fixed income indices and climate risk-focused indices, to meet investors' diverse needs. Ping An also developed a series of AI-ESG products focusing on corporate management, risk monitoring and analytics solutions for ESG and climate risk analysis, including portfolio sustainability footprint analysis, a portfolio adjustment tool, a sustainable funds screening tool, and climate risk asset pricing models to support ESG investment.

PADERC is a professional institution specializing in macroeconomics and policy research, using big data and artificial intelligence to provide insights on macroeconomic trends, including developments in ESG disclosures and ratings.

For the full report, click here.

About Ping An Group

Ping An Insurance (Group) Company of China, Ltd. ("Ping An") is a world-leading technology-powered retail financial services group. With over 210 million retail customers and 560 million Internet users, Ping An is one of the largest financial services companies in the world.

Ping An has two over-arching strategies, "pan financial assets" and "pan health care", which focus on the provision of financial and health care services through our integrated financial services platform and our five ecosystems of financial services, health care, auto services, real estate services and smart city services. Our "finance + technology" and "finance + ecosystem" strategies aim to provide customers and internet users with innovative and simple products and services using technology. As China's first joint stock insurance company, Ping An is committed to upholding the highest standards of corporate reporting and corporate governance. The Group is listed on the stock exchanges in Hong Kong and Shanghai.

In 2020, Ping An ranked 7th in the Forbes Global 2000 list and ranked 21st in the Fortune Global 500 list. Ping An also ranked 38th in the 2020 WPP Kantar Millward Brown BrandZTM Top 100 Most Valuable Global Brands list. For more information, please visit http://www.pingan.cn.

About Ping An Digital Economic Research Center

Ping An Digital Economic Research Center utilizes more than 50 TB high frequency data points, more than 30 years of historical data and more than 1.5 billion data points to drive research on the "AI + Macro Forecast" and provide insights and methods towards precise macroeconomic trends.

SOURCE Ping An Insurance (Group) Company of China, Ltd.

http://www.pingan.cn

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Ping An Uses Artificial Intelligence to Drive New ESG Investment Strategies - PRNewswire

Its time for your Farm of the Future Report. Im Tim Hammerich.

Harvest loss is a big deal for grower profitability and for sustainability of our resources.

Ganssle In 2019 in the United States in corn alone, it was a $1.4 billion problem. We left $1.4 billion worth of corn grain in the field last year. So it's a big deal.

Thats Craig Ganssle, CEO and founder of Farmwave, an artificial intelligence-based autonomous measurement tool. One application is mounting the tool on a combine to minimize yield loss.

Ganssle Right now, if Farmwave shows you X amount of header loss, you know, you're losing three to four bushels per acre, on iPad in the cab. It tells you your real time, here's what's happening and here's where it's coming from. And so you can make those changes. You can, whatever the changes would need to be on machinery: slow down, change reel speed, lift the head, whatever. But the real value, and what growers want to see, is this integrated in with their machinery. So we are in discussions with multiple OEMs about how to possibly do that and work towards automation. The future is getting that integrated into the machinery so it happens autonomously.

Other applications for the Farmwave AI tool include sprayer nozzle performance, application coverage, and disease and pest count and growth stage. And by 2022, they hope to be working with planters as well.

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Artificial Intelligence to Minimize Harvest Loss - AG INFORMATION NETWORK OF THE WEST - AGInfo Ag Information Network Of The West

Business

Professor Shannon Vallor, an expert in the challenging relationship between ethics and technology, reminds us that artificial intelligence is "human all the way down" - and therefore reflects the positives and negatives of human nature.

Prof Vallor, Baillie Gifford Chair in the Ethics of Data and AI at the Edinburgh Futures Institute, insists self-aware machines are not about to take over the world.

She says: "We have gone through a period where people like Stephen Hawking and Elon Musk have perhaps unwittingly misled the public about machines becoming self-aware or hyper-intelligent and enslaving humanity - and from a scientific perspective, thats just a complete fantasy at this point.

There is nothing mysterious or magical about AI - its something that is transforming our world but completely reflective of our own human strengths and weaknesses.

Professor Vallor is joined on the podcast by Nick Thomas and Kyle McEnery of Baillie Gifford. Nick Thomas highlights how access to data is going to be a key competitive advantage for business in the future, while Kyle McEnery describes his work on harnessing data and AI to make better decisions about where Baillie Gifford invests its clients money - and the potential for greater targeting of ethical investment.

Mr McEnery backs up Prof Vallor's comments about data and AI being fully human and says: There are a lot of biases in data that we need to be careful of and we try very, very, very hard to avoid those but its a constant challenge.

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Data and Artificial Intelligence: The Only Way is Ethics - The Scotsman

Forget whatever youve seen in science-fiction movies. Artificial intelligence, usually known as AI, is an umbrella term for computer programs that give machines human-like intelligence. As far as were concerned, it falls into two broad categories:

Narrow AI is what we have today. A narrow AI works well for specific tasks, for example identifying cat breeds in photographs, but its useless in all other areas. Just as you cant use the camera app on your phone to order something from Amazon, an AI designed to diagnose skin cancer from photographs of moles is completely useless for steering a self-driving car or recommending which movie to watch next.

In the future, we expect to have general AI. General AI will work across a range of areas, rather than being confined to one specific task. Were not there yet, but in a 2019 survey, 45% of technologists believed we would have it by 2060.

At the moment, the main technology under the AI umbrella is machine learning (ML). In machine learning, we provide structured and labelled training data, for example 1000 photographs of tugs and 1000 photographs of container ships. The computer analyses the data and learns to tell the difference between a photograph of a tug and a photograph of a container ship.

The main problem with machine learning is that, in most cases, we need carefully labelled training data. Unlabelled data is useless for standard machine learning. Converting thousands of entries in a database to the correct format then manually labelling them is expensive and time-consuming. In addition, machine learning systems usually need several smaller programs, known as models, to solve a problem. For example, you could build a system to look at photographs of oncoming ships and decide what action to take to avoid collision. In this case, one model could locate ships in a photograph and feed that information into the next model. The next model might identify the heading of the other vessel, while a third model would take that data and determine what action to take. You couldnt use machine learning to build a single model to look at the photograph and recommend a course of action.

Deep learning is a type of machine learning that uses artificial neural networks. The neural network is arranged in layers. Each layer processes the unstructured data, then inputs it into the next layer. Through this process, the system finds patterns in the data and eventually develops a model.

Neural networks accept unstructured and unlabelled data, and they resolve problems end-to-end rather than one part at a time. The downside is that they need a lot more training data and computing power, and they take longer to train than standard machine learning models.

Barriers to AI adoption range from fear of the unknown and laws not designed for AI, to a lack of appropriate training data and a shortage of data scientists.

More digitalised companies adopt AI at higher rates than less digitalised companies. This suggests that the digitalisation trend in the maritime industry could lead to wider adoption of AI systems.

Even without general AI, AI is creeping into all aspects of the maritime industry. Any repetitive, structured task has the potential to be carried out by a narrow AI model. Marine insurance, Fire detection from CCTV systems, AI-operated tugs, predictive maintenance, and fuel efficiency improvements are all moving towards AI-driven systems.

A study by the National Cargo Bureau found 6.5% of containers carrying dangerous goods had mis-declared cargo. To address this, Maersk is among the companies using AI screening tools to detect undeclared and mis-declared dangerous goods. HazCheck Detect, a new AI cargo screening tool, scans all booking details and highlights suspicious bookings. In the future, the same tool could screen cargoes to identify, for example, wildlife smuggling.

After demonstrating the worlds first fully-autonomous ferry in Finland in 2018, Rolls Royce is now using an AI system to provide deeper insight into the performance of installed ship equipment. This will lead to increased efficiency and reduced emissions.

Every year, 20% of vessels are diverted due to crew illness, and human error (including fatigue) accounts for around 75% to 90% of marine accidents. Communications provider KVH foresees the use of AI for seafarer health monitoring, to reduce accidents and diversions for crew illness or injury.

But illness and injury arent the only causes of human error: fatigue, intoxication, excitement and stress also lead to mistakes. Senseye uses high-resolution images of the iris to identify fatigue and intoxication, while Sensing Feeling uses real-time video to identify early signs of stress and fatigue.

As with any new technology, adoption of AI will be slow until it reaches a tipping point. As adoption of AI becomes widespread, many of the cultural barriers to AI are likely to disappear. For the last decade, the rate of AI adoption across all industries has been accelerating. Just as weve become accustomed to email and the internet, well soon take AI systems for granted too.

The bigger question is what impact AI will have on the industry. Maritime legislation, vessel manning, and much more are predicated on having a human in the loop. As autonomous ships become commonplace, we need to ensure that AI works for us.

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Artificial Intelligence in shipping and how it works - ShipInsight

The second phase of funded proposals has been announced for the Defence and Security Accelerator (DASA) Intelligent Ship competition to revolutionise military decision-making, mission planning and automation.

Phase 2 of Intelligent Ship, run by DASA on behalf of the Defence Science and Technology Laboratory (Dstl), sought novel technologies for use by the military in 2030 and beyond.

Nine innovative projects have been funded, sharing 3m.

With a focus on Artificial Intelligence (AI), the projects will support the evaluation and demonstration of a range of human-machine teams and their integration with an evaluation environment. Phase 2 will develop AI for wider application across defence platforms.

Julia Tagg, Dstl Project Technical Authority said:

The Intelligent Ship project aims to demonstrate ways of bringing together multiple AI applications to make collective decisions, with and without human operator judgement.

We hope that the use of AI in the future will lead to timely, more informed and trusted decision-making and planning, within complex operating and data environments. With applications for the Royal Navy and more broadly across defence, we are very excited to see what these Phase 2 projects might bring.

Rachel Solomons, DASA Delivery Manager said:

DASA is focussed on finding innovation to benefit the defence and security of the UK.

Artificial Intelligence and human-machine teaming are such innovations, and by taking this competition to Phase 2 we hope to help find solutions that could make a real difference to future decision making in defence.

The companies awarded funding for Phase 2 are:

Examples of proposals funded include an intelligent system for vessel power and propulsion machinery control to support the decision-making of the engineering crew, and an innovative mission AI prototype Agent for Decision-Making to support decision making during pre-mission preparation, mission execution and post mission analysis.

Phase one contracts were announced last year.

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3m to fund new wave of Artificial Intelligence for the military - GOV.UK

Dublin, Jan. 08, 2021 (GLOBE NEWSWIRE) -- The "Artificial intelligence in Healthcare Global Market - Forecast To 2027" report has been added to ResearchAndMarkets.com's offering.

Artificial intelligence in healthcare global market is expected to reach $35,323.5 million by 2027 growing at an exponential CAGR from 2020 to 2027 due to the gradual transition from volume to value-based healthcare

The surging need to accelerate and increase the efficiency of drug discovery and clinical trial processes, advancement of precision medicines, escalation of AI as a medical device, increasing prevalence of chronic, communicable diseases and escalating geriatric population and the increasing trend of acquisitions, collaborations, investments in the AI in healthcare market.

Artificial intelligence (AI) is the collection of computer programs or algorithms or software to make machines smarter and enable them to simulate human intelligence and perform various higher-order value-based tasks like visual perception, translation between languages, decision making and speech recognition.

The rapidly evolving vast and complex healthcare industry is slowly deploying AI solutions into its mainstream workflows to increase the productivity of various healthcare services efficiently without burdening the healthcare personnel, to streamline and optimize the various healthcare-associated administrative workflows, to mitigate the physician deficit and burnout issues effectively, to democratize the value-based healthcare services across the globe and to efficiently accelerate the drug discovery and development process.

Artificial intelligence in healthcare global market is classified based on the application, end-user and geography.

Based on the application, the market is segmented into Medical diagnosis, drug discovery, precision medicines, clinical trials, Healthcare Documentation management and others consisting of AI guided robotic surgical procedures and AI-enhanced medical device and pharmaceutical manufacturing processes.

The AI-powered Healthcare documentation management solutions segment accounted for the largest revenue in 2020 and is expected to grow at an exponential CAGR from 2020 to 2027. AI-enhanced Drug Discovery solutions segment is the fastest emerging segment, growing at an exponential CAGR from 2020 to 2027.

The artificial intelligence in healthcare global end-users market is grouped into Hospitals and Diagnostic Laboratories, Pharmaceutical companies, Research institutes and other end-users consisting of health insurance companies, medical device and pharmaceutical manufacturers and patients or individuals in the home-care settings.

Among these end users, Hospitals and Diagnostic Laboratories segment accounted for the largest revenue in 2020 and is expected to grow at an exponential CAGR during the forecasted period. Pharmaceutical companies segment is the fastest-growing segment, growing at an exponential CAGR from 2020 to 2027.

The artificial intelligence in healthcare global market by geography is segmented into North America, Europe, Asia-Pacific and the Rest of the world (RoW). North American region dominated the global artificial intelligence in healthcare market in 2020 and is expected to grow at an exponential CAGR from 2020 to 2027. The Asia-Pacific region is the fastest-growing region, growing at an exponential CAGR from 2020 to 2027.

The artificial intelligence in healthcare market is consolidated with the top five players occupying majority of the market share and the remaining minority share of the market being occupied by other players. Key Topics Covered:

1 Executive Summary

2 Introduction

3 Market Analysis3.1 Introduction3.2 Market Segmentation3.3 Factors Influencing Market3.3.1 Drivers and Opportunities3.3.1.1 Aiabetting the Transition from Volume Based to Value Based Healthcare3.3.1.2 Acceleration and Increasing Efficiency of Drug Discovery and Clinical Trials3.3.1.3 Escalation of Artificial Intelligence as a Medical Device3.3.1.4 Advancement of Precision Medicines3.3.1.5 Acquisitions, Investments and Collaborations to Open An Array of Opportunities for the Market to Flourish3.3.1.6 Increasing Prevalence of Chronic, Communicable Diseases and Escalating Geriatric Population3.3.2 Restraints and Threats3.3.2.1 Data Privacy Issues3.3.2.2 Reliability Issues and Black Box Reasoning Challenges3.3.2.3 Ethical Issues and Increasing Concerns Over Human Workforce Replacement3.3.2.4 Requirement of Huge Investment for the Deployment of AI Solutions3.3.2.5 Lack of Interoperability Between AI Vendors3.4 Regulatory Affairs3.4.1 International Organization for Standardization3.4.2 Astm International Standards3.4.3 U.S.3.4.4 Canada3.4.5 Europe3.4.6 Japan3.4.7 China3.4.8 India3.5 Porter's Five Force Analysis3.6 Clinical Trials3.7 Funding Scenario3.8 Regional Analysis of AI Start-Ups3.9 Artificial Intelligence in Healthcare FDA Approval Analysis3.10 AI Leveraging Key Deal Analysis3.11 AI Enhanced Healthcare Products Pipeline3.12 Patent Trends3.13 Market Share Analysis by Major Players3.13.1 Artificial Intelligence in Healthcare Global Market Share Analysis3.14 Artificial Intelligence in Healthcare Company Comparison Table by Application, Sub-Category, Product/Technology and End-User

4 Artificial Intelligence in Healthcare Global Market, by Application4.1 Introduction4.2 Medical Diagnosis4.3 Drug Discovery4.4 Clinical Trials4.5 Precision Medicine4.6 Healthcare Documentation Management4.7 Other Application

5 Artificial Intelligence in Healthcare Global Market, by End-User5.1 Introduction5.2 Hospitals and Diagnostic Laboratories5.3 Pharmaceutical Companies5.4 Research Institutes5.5 Other End-Users

6 Regional Analysis

7 Competitive Landscape7.1 Introduction7.2 Partnerships7.3 Product Launch7.4 Collaboration7.5 Up-Gradation7.6 Adoption7.7 Product Approval7.8 Acquisition7.9 Others

8 Major Companies8.1 Alphabet Inc. (Google Deepmind, Verily Lifesciences)8.2 General Electric Company8.3 Intel Corporation8.4 International Business Machines Corporation (IBM Watson)8.5 Koninklijke Philips N.V.8.6 Medtronic Public Limited Company8.7 Microsoft Corporation8.8 Nuance Communications Inc.8.9 Nvidia Corporation8.10 Welltok Inc.

For more information about this report visit https://www.researchandmarkets.com/r/dxs2ch

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

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Global Healthcare Artificial Intelligence Report 2020-2027: Market is Expected to Reach $35,323.5 Million - Escalation of AI as a Medical Device -...

It is safe to say that the closest thing next to human intelligence and abilities is artificial intelligence. Powered by its tools in machine learning, deep learning and neural network, there are so many things that existing artificial intelligence models are capable of. However do they dream or have psychedelic hallucinations like humans? Can the generative feature of deep neural networks experience dream like surrealism?

Neural networks are type of machine learning, focused on building trainable systems for pattern recognition and predictive modeling. Here the network is made up of layersthe higher the layer, the more precise the interpretation. Input data feed goes through all the layers, as the output of one layer is fed into the next layer. Just like neuron is the basic unit of the human brain, in a neural network, it is perceptron which forms the essential building block. A perceptron in a neural network accomplishes simple signal processing, and these are then connected into a large mesh network.

Generative Adversarial Network (GAN) is a type of neural network that was first introduced in 2014 by Ian Goodfellow. Its objective is to produce fake images that are as realistic as possible. GANs havedisrupted the development of fake images: deepfakes. The deep in deepfake is drawn from deep learning. To create deepfakes, neural networks are trained on multiple datasets. These dataset can be textual, audio-visual depending on the type of content we want to generate. With enough training, the neural networks will be able to create numerical representations the new content like a deepfake image. Next all we have to do is rewire the neural networks to map the image on to the target. Deepfake can also be created using autoencoders, which is a type of unsupervised neural network. In fact, in most of the deepfakes, autoencoders is the primary type of neural network used in their creation.

In 2015, a mysterious photo appeared onRedditshowing a monstrous mutant. This photo was later revealed to be a result of Google artificial neural network. Many pointed out that this inhumanly and scary appearing photo had striking resemblance to what one sees on psychedelic substances such as mushrooms or LSD.Basically, Google engineers decided that instead of asking the software to generate a specific image, they would simply feed it an arbitrary image and then ask it what it saw.

As per an abstract on Popular Science, Google used the artificial neural netowrk to amplify patterns it saw in pictures. Each artificial neural layer works on a different level of abstraction, meaning some picked up edges based on tiny levels of contrast, while others found shapes and colors. They ran this process to accentuate color and form, and then told the network to go buck wild, and keep accentuating anything it recognizes. In the lower levels of network, the results were similar to Van Gogh paintings: images with curving brush strokes, or images with Photoshop filters. After running these images through the higher levels, which recognize full images, like dogs, over and over, leaves transformed into birds and insects and mountain ranges transformed into pagodas and other disturbing hallucinating images.

Few years ago, Googles AI company DeepMindwas working on a new technology, which allows robots to dream in order to improve their rate of learning.

In a new article published in the scientific journalNeuroscience of Consciousness, researchers demonstrate how classic psychedelic drugs such as DMT, LSD, and psilocybin selectively change the function of serotonin receptors in the nervous system. And for this they gave virtual versions of the substances to neural network algorithms to see what happens.

Scientists from Imperial College London and the University of Geneva managed to recreate DMT hallucinations by tinkering around with powerful image-generating neural nets so that their usually-photorealistic outputs became distorted blurs. Surprisingly, the results were a close match to how people have described their DMT trips. As per Michael Schartner, a member of the International Brain Laboratory at Champalimaud Centre for the Unknown in Lisbon, The process of generating natural images with deep neural networks can be perturbed in visually similar ways and may offer mechanistic insights into its biological counterpart in addition to offering a tool to illustrate verbal reports of psychedelic experiences.

The objective behind this was to betteruncover the mechanismsbehind the trippy visions.

One basic difference between human brain and neural network is that our neurons communicate in multi-directional manner unlike feed forward mechanism of Googles neural network. Hence, what we see is a combination of visual data and our brains best interpretation of that data. This is also why our brain tends to fail in case of optical illusion. Further under the influence of drugs, our ability to perceive visual data is impaired, hence we tend to see psychedelic and morphed images.

While we have found answer to Do Androids Dream of Electric Sheep? by Philip K. Dick, an American sci-fi novelist; which is NO!, as artificial intelligence have bizarre dreams, we are yet to uncover answers about our dreams. Once we achieve that, we can program neural models to produce visual output or deepfakes as we expect. Besides, we may also apparently solve the mystery behind black box decisions.

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Does Artificial Intelligence Have Psychedelic Dreams and Hallucinations? - Analytics Insight