The IT Investment Priorities Shaping Healthcare Today – HealthTech Magazine

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Data Sits at the Forefront of Improving Patient Experiences

Healthcare, especially now, is continuously evolving to better serve its patients and offer quality care. In recent months, healthcare providers have scaled their telehealth offerings from a mere handful of appointments each week to hundreds of sessions.

Ensuring a positive patient experience with the technology, however, requires more than just a dedicated and well-trained care provider. Thats why healthcare survey respondents (45 percent) cited the importance of redesigning processes to align with new technology and developing an organizationwide strategy to improve patient experiences (42 percent) as top initiatives in the next two years.

Its also worth noting that half of healthcare respondents plan to include the real-time capture of patient feedback on their list of improvements over that time, followed by creating or improving the online experience for patients (48 percent) and providing ways to access information securely from anywhere (47 percent). These investments go hand in hand with what experts recommend for transforming patient telehealth experiences.

And, of course, to make virtual care work seamlessly for patients, healthcare organizations understand theyll need to invest heavily in data and analytics technologies (61 percent), mobile apps (48 percent) and mobile devices (40 percent).

MORE FROM HEALTHTECH: Learn why predictive analytics are critical to better care delivery.

The scale at which telehealth, virtual care and remote work have grown during the pandemic is unprecedented. And supporting and sustaining this type of growth can only be achieved through a modern IT infrastructure.

Its good news, then, that an overwhelming majority of healthcare respondents feel that their organizations current technology infrastructure is either very well aligned (44 percent) or somewhat well aligned (48 percent) with its future vision and goals. In fact, only 8 percent of healthcare individuals responded that theyre not very well aligned.

That preparedness hasnt stopped organizations from looking to the future, though: 46 percent of respondents cited IT cost management as a priority to help them meet their business objectives over the next two years, followed by cloud monitoring/management (45 percent) and developing a long-term IT roadmap (41 percent).

Further supporting healthcares good positioning, respondents in IT roles expect that two years from now, 79 percent of their total IT environments will leverage cloud delivery models, preparing them for anything that might come their way.

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The IT Investment Priorities Shaping Healthcare Today - HealthTech Magazine

Provider bias in health care | TheHill – The Hill

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Earlyfearsthat the lives of the56 million Americansliving with a disability would be at risk of disparate care during theCOVID-19pandemic are coming to fruition. The death of a 46-year-old person with a disability from COVID-19 exposes the frightening reality that many people with disabilities live with. If they contract the virus, the provider's disability biases can result in inequitable care.

Last month, Michael Hickson, a 46-year-old man, was refused medical treatment for the virus, and life-sustaining care was removed by medical staff atSt. David's South Austin Medical Center in Texas. Six days later, he died. The fact that Mr. Hickson was a person with a disability was thejustificationhis physician gave.

In May of 2017, Mr. Hickson suffered a sudden cardiac arrest while driving his wife Melissa to work, resulting in brain damage that caused him to lose the ability to move.

I have worked for decades as an occupational therapist with people recovering from brain injuries similar to Mr. Hickson's and vow the majority continue to hold a vital and central role in the lives of spouses and children. I have also witnessed people who recover over an extended period, returning home and participating in everyday activities in ways their medical team thought impossible.

No choice was given to Mr. Hickson's family during the five-minute conversation with his physician describing the hospital's rationale for removing his lifesaving nutrition lines. In astatementon its website St. David describes a court-appointed guardian who had the decision-making power over his family and concluded with medical team collaboration to discontinue care. Astatementby the hospital's CEO describes how ill Mr. Hickson was and the legal processes that gave them the power to make this decision.

The local chapter ofADAPThas worked to expose this action as local and national media attention is inexplicably almost non-existent. This case is slowly gaining recognition only because the familyrecorded the sessionwith the physician and reached out to apro-lifeactivism group to help share their story.

While the nation cries out to expose murders ofBlack livesat the hands of police, there must be an equal outcry for ending disabled lives at the hands of state-appointed guardians. This is especially troubling when family voices are overpowered.

The reasons given for withholding treatment are blatantly and illegally discriminatory under recent federalHHS Office of Civil Rights COVID triage rulings.The rulings explicitly state that crisis standards of care must ensure that the criteria for providing care, including lifesaving care, does not discriminate against persons based on disability and age.Storiesof extreme, extended, and expensive lifesaving approaches for COVID-19 victims are heard daily for people without disability.

There are ethical concepts at conflict regarding the course ofremoving artificial life support technologywhen a person is considered to bebrain dead. There are alsodo-not-resuscitate(DNR) orders informing staff that cardiopulmonary resuscitation (CPR) should not be performed in the event of the death of a person in their care. Neither scenario fits in the case of Mr. Hickson. Before becoming ill, he engaged with his wife and family, and he did not die of natural causes. The hospital, under orders from the state of Texas, withdrew care.

The civil rights rulings from the Department of Health and Human Services specifies that patients who require additional treatment or resources due to age or disability should not be given a lower priority to receive lifesaving care. The Office of Civil Rights of this country is in place to help enforce that all citizens of this country are entitled to the same level of care. BothSection 504 of the Rehabilitation Act of 1973andTitle II of the Americans with Disabilities Act(ADA) of 1990 prohibit health care providers and institutions from discriminating against persons with disabilities in the provision of services based on their disability. This law exists because of historicalabuses and atrocitiesby thestate toward groups of society deemed unworthy. A history that may be repeating itself if Mr. Hickson's incident is, in fact, not an isolated occurrence, which many people with disabilities fear it is not.

If we are at a place in history that we can question and answer who qualifies for care and is selected by the state to die, our country is moving toward a scary future. The lasting effects of COVID-19 may include political and policy changes that emerge that support this type of practice by a provider or state, or condemn it.

Laura VanPuymbrouck, Ph.D., OTR/L, is an assistant professor in the College of Health Sciences atRush University, Chicago, in theDepartment of Occupational Therapy. Her research examines the health care and health disparities of people with disabilities.

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Provider bias in health care | TheHill - The Hill

Pandemic hits women harder in jobs, health care – KTAB – BigCountryHomepage.com

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Women more likely to be exposed to virus as they're on front lines

by: Alexandra Limon

WASHINGTON (Nexstar) History shows economic recessions tend to worsen inequities that already exist. Statistics show the pandemic is having a greater impact on women than men.

Congresswoman Dina Titus said the coronavirus recession is just making things worse. Data from the US Labor Department shows women experienced higher unemployment rates than men in April, May and June. Women are also more likely to be exposed to the virus because they tend to work in front line jobs.

Women already make less than men, we know that. And women of color make even less than men, for the same work, for the same amount of time, said Titus, a Nevada Democrat. About two-thirds of health care workers, two-thirds of social workers, also grocery store and fast food workers all are women.

Dr. William Spriggs, the chief economist for the AFL-CIO, said those women are also less likely to have access to proper health care.

A very frightening share of women who show up to work and report that they have symptoms, because they fear losing their job, Spriggs said.

But White House economic adviser Larry Kudlow said reopening schools is one way to help women.

Traditional families, too, but single moms who have to work but if the kids are home Kudlow said.

The solution isnt simple.

More than 75% of teachers are women. The Kaiser Family Foundation said one in four teachers may be at risk of severe illness from COVID-19.

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UMass Memorial Health Care and Israeli company to collaborate on new solution to prevent avoidable blindness – MassLive.com

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UMass Memorial Health Care has announced that it is partnering with a health company in Israel to co-develop a new paradigm designed to prevent avoidable blindness and save lives for high-risk patients.

The Worcester-based health care system is partnering with AEYE Health, based in Tel Aviv, using a grant from the Binational Industrial Research and Development Foundation to develop the joint product. The Board of Governors of the Israel-U.S. organization have approved $8 million in funding for ten new projects between U.S. and Israeli companies.

UMass Memorial and AEYE Health plan to use advanced machine learning techniques to allow the product to provide an immediate automatic diagnosis from fundus images, meant to be deployed in hospitals and health networks nationwide, UMass Memorial wrote in a statement.

While over a billion people are at high-risk for retinal diseases and need an annual check (>75M in the USA), unfortunately, over 75% are not screened as the interpretation is expensive and impractical, said Dr. Zack Dvey-Aharon, the co-founder and CEO of AEYE Health. Using our system, clinicians can detect a variety of medical conditions and prevent blindness.

The system can provide diagnoses including commonly diagnosed conditions, like diabetic retinopathy and glaucoma, or for more systemic issues, like Alzheimers disease, the statement said.

We are truly grateful to receive this funding that will absolutely further and enhance the cause of patient eye care in our region, said Dr. Shlomit Schaal, the chair of the Department of Ophthalmology & Visual Sciences at UMass Memorial. This patient-friendly technology empowers clinicians with real-time information that will ultimately lead to timelier and better informed diagnoses.

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UMass Memorial Health Care and Israeli company to collaborate on new solution to prevent avoidable blindness - MassLive.com

Apple, Biden, Musk and other high-profile Twitter accounts hacked in crypto scam – TechCrunch

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A number of high-profile Twitter accounts were simultaneously hacked on Wednesday by attackers who used the accounts some with millions of followers to spread a cryptocurrency scam.

Apple, Elon Musk and Joe Biden were among the accounts compromised in a broadly targeted hack that remained mysterious hours after taking place. Those accounts and many others posted a message promoting the address of a bitcoin wallet with the claim that the amount of any payments made to the address would be doubled and sent back a known cryptocurrency scam technique.

In the hours following the initial scam posts, Kim Kardashian West, Jeff Bezos, Bill Gates, Barack Obama, Wiz Khalifa, Warren Buffett, YouTuber MrBeast, Wendys, Uber, CashApp and Mike Bloomberg also posted the cryptocurrency scam.

Screenshot via Twitter

While were still learning more specifics about how the hack went down, we can report that the hacker leveraged an internal Twitter admin tool to gain access to the high-profile accounts. That reporting was soon confirmed by Twitters own account of what happened. On Wednesday evening, the company tweeted that a coordinated social engineering attack on employees gave a hacker access to internal systems and tools.

Before the scope of the incident became clear, the hack appeared to focus on cryptocurrency-focused accounts. In an initial wave of scam posts, @bitcoin, @ripple, @coindesk, @coinbaseand @binance were hacked with the same message: We have partnered with CryptoForHealth and are giving back 5000 BTC to the community, followed by a link to a website.

The linked site was quickly pulled offline. Kristaps Ronka, chief executive of Namesilo, the domain registrar used by the scammers, told TechCrunch that the company suspended the domain on the first report it received. Hacked accounts shifted to sharing multiple bitcoin wallet addresses as the incident went on, making things more difficult to track.

Twitter first acknowledged the situation at 2:45 p.m. PT Wednesday afternoon, referring to it as a security incident.

At first, it appeared that some of the compromised accounts were back under their owners control as tweets were quickly deleted. But then, Elon Musks account tweeted hi after his initial tweet with the scam was deleted. The hi tweet also disappeared.

Twitter users reported seeing error messages on the platform as the situation went on. TechCrunch reporter Natasha Mascarenhas saw this error (see below) when she tried to create a threaded tweet. TechCrunch reporter Sarah Perez saw a similar error when trying to post a normal tweet. Both have verified accounts.

Twitter error message (Image: TechCrunch)

As the issues continued, many verified Twitter users also reported being unable to tweet. Around 3:15 p.m. PT, the official Twitter Support account confirmed [Users] may be unable to Tweet or reset your password while we review and address this incident. By Wednesday evening, Twitter said that most tweeting should be back to normal but functionality may come and go as the company continue[s] working on a fix.

It became clear early on that this situation was not the case of a single account being compromised as weve seen in the past, but something else altogether. Even Apple, a company known for robust security, somehow fell victim to the scheme.

Apples account was also hacked. This was the accounts first tweet. (Image: TechCrunch)

Many high profile accounts were quickly hijacked in rapid succession Wednesday afternoon, including @elonmusk, the eccentric Twitter-obsessed tech figure with a notoriously engaged fanbase. A scam tweet posted to the Tesla and SpaceX founders account simply directed users to send bitcoin to a certain address under the guise that he will double any payment a known cryptocurrency scam technique. Musks account appeared to remain compromised for some time after the initial message, with follow-up posts claiming followers were sending money to the suspicious address.

Tesla and SpaceX founder Elon Musk had his Twitter account hacked to spread a cryptocurrency scam. (Image: TechCrunch)

Some Democratic political figures were also hacked as part of the cryptocurrency scam, including Barack Obama, Joe Biden and Mike Bloomberg. An official from the Biden campaign told TechCrunch that Twitter locked down the former vice presidents account immediately after it was compromised and the campaign remains in close contact with Twitter on the issue. At the time of writing, no accounts belonging to Republican politicians appear to have been hacked.

Barack Obama had his Twitter account hacked to spread a cryptocurrency scam. (Image: TechCrunch)

Wiz Khalifas account was also compromised, as was the Twitter account of popular YouTuber MrBeast, who often posts giveaways, making his re-post of the bitcoin address particularly likely to drive followers to the scam.

The hack also hit legendary investor Warren Buffet, a prominent and harsh critic of cryptocurrencies like bitcoin. I dont have any cryptocurrency and I never will, Buffet told CNBC in February.

While the scope of Wednesdays Twitter hack is unprecedented on the social network, the kinds of scams the hacked accounts promoted are common. Scammers take over high-profile Twitter accounts using breached or leaked passwords and post messages that encourage users to post their cryptocurrency funds to a particular address under the guise that theyll double their investment. In reality, its simple theft, but its a scam that works.

The main blockchain address used on the scam site had already collected more than 12.5 bitcoin some $116,000 in USD and its going up by the minute.

A spokesperson for Binance told TechCrunch: The security team is actively investigating the situation of this coordinated attack on the crypto industry. Several other companies affected by the account hacks did not immediately respond to a request for comment.

Its not immediately known how the account hacks took place. Security researchers, however, found that the attackers had fully taken over the victims accounts, and also changed the email address associated with the account to make it harder for the real user to regain access.

Scammers frequently reply to high-profile accounts, like celebrities and public figures, to hijack the conversation and hoodwink unsuspecting victims. Twitter typically shuts these accounts down pretty fast.

A Twitter spokesperson, when reached, said the company was looking into the matter but didnt immediately comment.

This story is developing. Stay tuned for updates.

Below are screenshots of some of the hacked accounts.

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Apple, Biden, Musk and other high-profile Twitter accounts hacked in crypto scam - TechCrunch

Bitcoin Exchanges And The Cryptocurrency World Was Just Rocked – JD Supra

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In an unexpected to say the least case of first impression, the United States Court of Appeals for the Fifth Circuit, essentially, blew away the privacy doors of the cryptocurrency world when it forced a Bitcoin exchange to disclose user data to the federal government without being served a warrant. See USA v. Gratkowski, Case number 19-50492, (5th Cir. 2020). This Bitcoin exchange use blockchain technology that records every transaction in a publicly accessible ledger, but the persons owning the actual Bitcoin addresses are not known.

The appellate court found that the government could subpoena a cryptocurrency exchange, and obtain records since there was no violation of the defendants Fourth Amendment rights. The court reasoned that users of the digital coin exchanges have no greater privacy rights than those people who have accounts at ordinary banks. The court also held that Bitcoin traders have no expectation of privacy for information published on the public blockchain.

This decision also implicated the United States Supreme Courts recent decision requiring a warrant to access cellphone records in Carpenter v. United States. In this case, however, the court said only a subpoena was necessary because it was similar to bank records where there is not necessarily a Fourth Amendment protection. The court also indicated that no one considered Bitcoins to be as central to someones daily life like cellphones.

It would appear that, despite the well-known privacy benefits of blockchain technology, this court apparently believes these exchanges fall under the third-party doctrine, whereby there is no expectation of privacy when a party turns over their information voluntarily to a third party, including, but not limited to, banks. The court found that both traditional banks and cryptocurrency exchanges would be subject to the Bank Secrecy Act of 1970, the statutory authority requiring financial institutions to turn over financial records.

Nonetheless, this decision may have a chilling effect on the blockchain and cryptocurrency industry. Many participants have been drawn to this medium because it offers high degree of privacy. It is possible that this decision may cause a great deal of anxiety in this area.

As a result, it is more likely that law enforcement authoritiescivil and criminalwill be seeking information from Bitcoin exchanges. Conversely, it is also likely that Bitcoin exchanges will probably publish less information and seek enhanced privacy protections. Accordingly, these issues should be carefully discussed with counsel when proceeding in the future.

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Bitcoin Exchanges And The Cryptocurrency World Was Just Rocked - JD Supra

Brave New Coin to Develop Cryptocurrency Indices that Toronto Futures Options Swaps Exchange will Use for Cash-Settled Options Trading – Crowdfund…

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Brave New Coin, a crypto-asset trading, research, and data firm, has agreed to a multi-year partnership with Toronto Futures Options Swaps Exchange or tFOSE, a derivatives exchange and clearinghouse thats in the process of obtaining regulatory approval in Canada.

Brave New Coin will be designing, calculating, and administering several different cryptocurrency indices that will be used to power cash-settled options trading on tFOSE.

An options contract is an agreement between two consenting parties to carry out a potential transaction with a particular asset at a predetermined price and date. Purchasing an options contract provides the right (and not the obligation) to buy or sell the underlying asset.

As mentioned in a release shared with CI:

Canada has not yet made significant progress in bringing institutional-grade cryptocurrency products to the market. Brave New Coins indices will enable tFOSEs clients both in Canada and globally to trade crypto derivatives on a fully-regulated Canadian exchange. This allows traders to diversify their portfolios and exposure, hedge risk, and access an emerging asset class without having to directly hold the underlying cryptocurrency as they are cash-settled products.

James Beattie, President and CEO at tFOSE, stated that after conducting relevant research and performing due diligence, tFOSE chose Brave New Coin for generating cryptocurrency market data and indices.

Beattie added that Brave New Coin meets all of his companys requirements, which include a unique approach to designing indices that should help tFOSe satisfy the needs of its retail and institutional investors.

Fran Strajnar, CEO and Founder of Brave New Coin, remarked:

The crypto ecosystem is maturing and demand for regulated investment products from institutional markets is growing. Weve dedicated our company to building products that bring institutional-grade services to this emerging asset class.

Brave New Coin offers various data and index solutions to its partners which include NASDAQ, Amazon Alexa, BTSE.com, TPICAP and Dow Jones Factiva. When people ask Amazons Alexa for the price of any digital currency, her answer reportedly comes from Brave New Coins data engine.

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Brave New Coin to Develop Cryptocurrency Indices that Toronto Futures Options Swaps Exchange will Use for Cash-Settled Options Trading - Crowdfund...

Cryptocurrency Exchanges Market 2020 Global Trend, Growth, Demand, Size, Segmentation and Opportunities Analysis and Forecast To 2024 – 3rd Watch News

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According to this study, over the next five years the Cryptocurrency Exchanges market will register a xx% CAGR in terms of revenue, the global market size will reach US$ xx million by 2024, from US$ xx million in 2019. In particular, this report presents the global revenue market share of key companies in Cryptocurrency Exchanges business, shared in Chapter 3.

This report presents a comprehensive overview, market shares and growth opportunities of Cryptocurrency Exchanges market by product type, application, key companies and key regions.

Get a PDF sample of this report @https://www.orbisresearch.com/contacts/request-sample/2902396

The report also presents the market competition landscape and a corresponding detailed analysis of the major vendor/manufacturers in the market. The key manufacturers covered in this report: Breakdown data in in Chapter 3.

This study considers the Cryptocurrency Exchanges value generated from the sales of the following segments:

Segmentation by product type: breakdown data from 2014 to 2019 in Section 2.3; and forecast to 2024 in section 10.7.

Segmentation by application: breakdown data from 2014 to 2019, in Section 2.4; and forecast to 2024 in section 10.8.

Access the complete report @https://www.orbisresearch.com/reports/index/global-cryptocurrency-exchanges-market-growth-status-and-outlook-2019-2024

This report also splits the market by region: Breakdown data in Chapter 4, 5, 6, 7 and 8.

In addition, this report discusses the key drivers influencing market growth, opportunities, the challenges and the risks faced by key players and the market as a whole. It also analyzes key emerging trends and their impact on present and future development.

Research objectives

Major Points from Table of Content:

1 Scope of the Report

2 Executive Summary

3 Global Cryptocurrency Exchanges by Players

4 Cryptocurrency Exchanges by Regions

5 Americas

6 APAC

7 Europe

8 Middle East & Africa

9 Market Drivers, Challenges and Trends

10 Global Cryptocurrency Exchanges Market Forecast

11 Key Players Analysis

11.1 Binance

11.1.1 Company Details

11.1.2 Cryptocurrency Exchanges Product Offered

11.1.3 Binance Cryptocurrency Exchanges Revenue, Gross Margin and Market Share (2017-2019)

11.1.4 Main Business Overview

11.1.5 Binance News

11.2 Coinbase

11.2.1 Company Details

11.2.2 Cryptocurrency Exchanges Product Offered

11.2.3 Coinbase Cryptocurrency Exchanges Revenue, Gross Margin and Market Share (2017-2019)

11.2.4 Main Business Overview

11.2.5 Coinbase News

11.3 Poloniex

11.3.1 Company Details

11.3.2 Cryptocurrency Exchanges Product Offered

11.3.3 Poloniex Cryptocurrency Exchanges Revenue, Gross Margin and Market Share (2017-2019)

11.3.4 Main Business Overview

11.3.5 Poloniex News

Continue

12 Research Findings and Conclusion

Have any query? Feel free to ask us @https://www.orbisresearch.com/contacts/enquiry-before-buying/2902396

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Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

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Artificial intelligence – Wikipedia

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Intelligence demonstrated by machines

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals. Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving".

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect.[3] A quip in Tesler's Theorem says "AI is whatever hasn't been done yet."[4] For instance, optical character recognition is frequently excluded from things considered to be AI,[5] having become a routine technology.[6] Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go),[8] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.[9]

Artificial intelligence was founded as an academic discipline in 1955, and in the years since has experienced several waves of optimism,[10][11] followed by disappointment and the loss of funding (known as an "AI winter"),[12][13] followed by new approaches, success and renewed funding.[11][14] For most of its history, AI research has been divided into sub-fields that often fail to communicate with each other.[15] These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"),[16] the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences.[17][18][19] Sub-fields have also been based on social factors (particular institutions or the work of particular researchers).[15]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[16] General intelligence is among the field's long-term goals.[20] Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.

The field was founded on the assumption that human intelligence "can be so precisely described that a machine can be made to simulate it".[21] This raises philosophical arguments about the mind and the ethics of creating artificial beings endowed with human-like intelligence. These issues have been explored by myth, fiction and philosophy since antiquity.[22] Some people also consider AI to be a danger to humanity if it progresses unabated.[23][24] Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.[25]

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[26][14]

Thought-capable artificial beings appeared as storytelling devices in antiquity,[27] and have been common in fiction, as in Mary Shelley's Frankenstein or Karel apek's R.U.R. (Rossum's Universal Robots).[28] These characters and their fates raised many of the same issues now discussed in the ethics of artificial intelligence.[22]

The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight, that digital computers can simulate any process of formal reasoning, is known as the ChurchTuring thesis.[29] Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to consider the possibility of building an electronic brain. Turing proposed changing the question from whether a machine was intelligent, to "whether or not it is possible for machinery to show intelligent behaviour".[30] The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons".

The field of AI research was born at a workshop at Dartmouth College in 1956,[32] where the term "Artificial Intelligence" was coined by John McCarthy to distinguish the field from cybernetics and escape the influence of the cyberneticist Norbert Wiener.[33] Attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research.[34] They and their students produced programs that the press described as "astonishing": computers were learning checkers strategies (c. 1954)[36] (and by 1959 were reportedly playing better than the average human),[37] solving word problems in algebra, proving logical theorems (Logic Theorist, first run c. 1956) and speaking English.[38] By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense[39] and laboratories had been established around the world.[40] AI's founders were optimistic about the future: Herbert Simon predicted, "machines will be capable, within twenty years, of doing any work a man can do". Marvin Minsky agreed, writing, "within a generation... the problem of creating 'artificial intelligence' will substantially be solved".[10]

They failed to recognize the difficulty of some of the remaining tasks. Progress slowed and in 1974, in response to the criticism of Sir James Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the U.S. and British governments cut off exploratory research in AI. The next few years would later be called an "AI winter",[12] a period when obtaining funding for AI projects was difficult.

In the early 1980s, AI research was revived by the commercial success of expert systems,[42] a form of AI program that simulated the knowledge and analytical skills of human experts. By 1985, the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research.[11] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer-lasting hiatus began.[13]

The development of metaloxidesemiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) transistor technology, enabled the development of practical artificial neural network (ANN) technology in the 1980s. A landmark publication in the field was the 1989 book Analog VLSI Implementation of Neural Systems by Carver A. Mead and Mohammed Ismail.[43]

In the late 1990s and early 21st century, AI began to be used for logistics, data mining, medical diagnosis and other areas.[26] The success was due to increasing computational power (see Moore's law and transistor count), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[44] Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997.

In 2011, a Jeopardy! quiz show exhibition match, IBM's question answering system, Watson, defeated the two greatest Jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. Faster computers, algorithmic improvements, and access to large amounts of data enabled advances in machine learning and perception; data-hungry deep learning methods started to dominate accuracy benchmarks around 2012.[47] The Kinect, which provides a 3D bodymotion interface for the Xbox 360 and the Xbox One, uses algorithms that emerged from lengthy AI research[48] as do intelligent personal assistants in smartphones.[49] In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps.[8][50] In the 2017 Future of Go Summit, AlphaGo won a three-game match with Ke Jie,[51] who at the time continuously held the world No. 1 ranking for two years.[52][53] This marked the completion of a significant milestone in the development of Artificial Intelligence as Go is a relatively complex game, more so than Chess.

According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating the improvements of AI since 2012 supported by lower error rates in image processing tasks.[54] He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[14] Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people.[54] In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes".[55][56] Around 2016, China greatly accelerated its government funding; given its large supply of data and its rapidly increasing research output, some observers believe it may be on track to becoming an "AI superpower".[57][58] However, it has been acknowledged that reports regarding artificial intelligence have tended to be exaggerated.[59][60][61]

Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] A more elaborate definition characterizes AI as "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation."[62]

A typical AI analyzes its environment and takes actions that maximize its chance of success.[1] An AI's intended utility function (or goal) can be simple ("1 if the AI wins a game of Go, 0 otherwise") or complex ("Perform actions mathematically similar to ones that succeeded in the past"). Goals can be explicitly defined or induced. If the AI is programmed for "reinforcement learning", goals can be implicitly induced by rewarding some types of behavior or punishing others.[a] Alternatively, an evolutionary system can induce goals by using a "fitness function" to mutate and preferentially replicate high-scoring AI systems, similar to how animals evolved to innately desire certain goals such as finding food. Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data. Such systems can still be benchmarked if the non-goal system is framed as a system whose "goal" is to successfully accomplish its narrow classification task.[65]

AI often revolves around the use of algorithms. An algorithm is a set of unambiguous instructions that a mechanical computer can execute.[b] A complex algorithm is often built on top of other, simpler, algorithms. A simple example of an algorithm is the following (optimal for first player) recipe for play at tic-tac-toe:

Many AI algorithms are capable of learning from data; they can enhance themselves by learning new heuristics (strategies, or "rules of thumb", that have worked well in the past), or can themselves write other algorithms. Some of the "learners" described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world[citation needed]. These learners could therefore derive all possible knowledge, by considering every possible hypothesis and matching them against the data. In practice, it is seldom possible to consider every possibility, because of the phenomenon of "combinatorial explosion", where the time needed to solve a problem grows exponentially. Much of AI research involves figuring out how to identify and avoid considering a broad range of possibilities unlikely to be beneficial.[67] For example, when viewing a map and looking for the shortest driving route from Denver to New York in the East, one can in most cases skip looking at any path through San Francisco or other areas far to the West; thus, an AI wielding a pathfinding algorithm like A* can avoid the combinatorial explosion that would ensue if every possible route had to be ponderously considered.[69]

The earliest (and easiest to understand) approach to AI was symbolism (such as formal logic): "If an otherwise healthy adult has a fever, then they may have influenza". A second, more general, approach is Bayesian inference: "If the current patient has a fever, adjust the probability they have influenza in such-and-such way". The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: "After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza". A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial "neurons" that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to "reinforce" connections that seemed to be useful. These four main approaches can overlap with each other and with evolutionary systems; for example, neural nets can learn to make inferences, to generalize, and to make analogies. Some systems implicitly or explicitly use multiple of these approaches, alongside many other AI and non-AI algorithms; the best approach is often different depending on the problem.[71]

Learning algorithms work on the basis that strategies, algorithms, and inferences that worked well in the past are likely to continue working well in the future. These inferences can be obvious, such as "since the sun rose every morning for the last 10,000 days, it will probably rise tomorrow morning as well". They can be nuanced, such as "X% of families have geographically separate species with color variants, so there is a Y% chance that undiscovered black swans exist". Learners also work on the basis of "Occam's razor": The simplest theory that explains the data is the likeliest. Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.

Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is. Besides classic overfitting, learners can also disappoint by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers don't determine the spatial relationship between components of the picture; instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects. Faintly superimposing such a pattern on a legitimate image results in an "adversarial" image that the system misclassifies.[c][74][75][76]

Compared with humans, existing AI lacks several features of human "commonsense reasoning"; most notably, humans have powerful mechanisms for reasoning about "nave physics" such as space, time, and physical interactions. This enables even young children to easily make inferences like "If I roll this pen off a table, it will fall on the floor". Humans also have a powerful mechanism of "folk psychology" that helps them to interpret natural-language sentences such as "The city councilmen refused the demonstrators a permit because they advocated violence". (A generic AI has difficulty discerning whether the ones alleged to be advocating violence are the councilmen or the demonstrators.)[79][80][81] This lack of "common knowledge" means that AI often makes different mistakes than humans make, in ways that can seem incomprehensible. For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.[82][83][84]

The cognitive capabilities of current architectures are very limited, using only a simplified version of what intelligence is really capable of. For instance, the human mind has come up with ways to reason beyond measure and logical explanations to different occurrences in life. What would have been otherwise straightforward, an equivalently difficult problem may be challenging to solve computationally as opposed to using the human mind. This gives rise to two classes of models: structuralist and functionalist. The structural models aim to loosely mimic the basic intelligence operations of the mind such as reasoning and logic. The functional model refers to the correlating data to its computed counterpart.[85]

The overall research goal of artificial intelligence is to create technology that allows computers and machines to function in an intelligent manner. The general problem of simulating (or creating) intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention.[16]

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions.[86] By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[87]

These algorithms proved to be insufficient for solving large reasoning problems because they experienced a "combinatorial explosion": they became exponentially slower as the problems grew larger.[67] Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using fast, intuitive judgments.[88]

Knowledge representation[89] and knowledge engineering[90] are central to classical AI research. Some "expert systems" attempt to gather explicit knowledge possessed by experts in some narrow domain. In addition, some projects attempt to gather the "commonsense knowledge" known to the average person into a database containing extensive knowledge about the world. Among the things a comprehensive commonsense knowledge base would contain are: objects, properties, categories and relations between objects;[91] situations, events, states and time;[92] causes and effects;[93] knowledge about knowledge (what we know about what other people know);[94] and many other, less well researched domains. A representation of "what exists" is an ontology: the set of objects, relations, concepts, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language.[95] The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge[96] by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval,[97] scene interpretation,[98] clinical decision support,[99] knowledge discovery (mining "interesting" and actionable inferences from large databases),[100] and other areas.[101]

Among the most difficult problems in knowledge representation are:

Intelligent agents must be able to set goals and achieve them.[108] They need a way to visualize the futurea representation of the state of the world and be able to make predictions about how their actions will change itand be able to make choices that maximize the utility (or "value") of available choices.[109]

In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.[110] However, if the agent is not the only actor, then it requires that the agent can reason under uncertainty. This calls for an agent that can not only assess its environment and make predictions but also evaluate its predictions and adapt based on its assessment.[111]

Multi-agent planning uses the cooperation and competition of many agents to achieve a given goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[112]

Machine learning (ML), a fundamental concept of AI research since the field's inception,[113] is the study of computer algorithms that improve automatically through experience.[114][115]

Unsupervised learning is the ability to find patterns in a stream of input, without requiring a human to label the inputs first. Supervised learning includes both classification and numerical regression, which requires a human to label the input data first. Classification is used to determine what category something belongs in, and occurs after a program sees a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.[115] Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be viewed as learning a function that maps from the text of an email to one of two categories, "spam" or "not spam". Computational learning theory can assess learners by computational complexity, by sample complexity (how much data is required), or by other notions of optimization.[116] In reinforcement learning[117] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space.

Natural language processing[118] (NLP) allows machines to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts. Some straightforward applications of natural language processing include information retrieval, text mining, question answering[119] and machine translation.[120] Many current approaches use word co-occurrence frequencies to construct syntactic representations of text. "Keyword spotting" strategies for search are popular and scalable but dumb; a search query for "dog" might only match documents with the literal word "dog" and miss a document with the word "poodle". "Lexical affinity" strategies use the occurrence of words such as "accident" to assess the sentiment of a document. Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level. Beyond semantic NLP, the ultimate goal of "narrative" NLP is to embody a full understanding of commonsense reasoning.[121] By 2019, transformer-based deep learning architectures could generate coherent text.[122]

Machine perception[123] is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Applications include speech recognition,[124] facial recognition, and object recognition.[125] Computer vision is the ability to analyze visual input. Such input is usually ambiguous; a giant, fifty-meter-tall pedestrian far away may produce the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its "object model" to assess that fifty-meter pedestrians do not exist.[126]

AI is heavily used in robotics.[127] Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[128] A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient's breathing body, pose a greater challenge. Motion planning is the process of breaking down a movement task into "primitives" such as individual joint movements. Such movement often involves compliant motion, a process where movement requires maintaining physical contact with an object.[130][131] Moravec's paradox generalizes that low-level sensorimotor skills that humans take for granted are, counterintuitively, difficult to program into a robot; the paradox is named after Hans Moravec, who stated in 1988 that "it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility".[132][133] This is attributed to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years.[134]

Moravec's paradox can be extended to many forms of social intelligence.[136][137] Distributed multi-agent coordination of autonomous vehicles remains a difficult problem.[138] Affective computing is an interdisciplinary umbrella that comprises systems which recognize, interpret, process, or simulate human affects. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[142]

In the long run, social skills and an understanding of human emotion and game theory would be valuable to a social agent. The ability to predict the actions of others by understanding their motives and emotional states would allow an agent to make better decisions. Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate humancomputer interaction.[143] Similarly, some virtual assistants are programmed to speak conversationally or even to banter humorously; this tends to give nave users an unrealistic conception of how intelligent existing computer agents actually are.[144]

Historically, projects such as the Cyc knowledge base (1984) and the massive Japanese Fifth Generation Computer Systems initiative (19821992) attempted to cover the breadth of human cognition. These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI. Nowadays, most current AI researchers work instead on tractable "narrow AI" applications (such as medical diagnosis or automobile navigation).[145] Many researchers predict that such "narrow AI" work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[20][146] Many advances have general, cross-domain significance. One high-profile example is that DeepMind in the 2010s developed a "generalized artificial intelligence" that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[147][148][149] Besides transfer learning,[150] hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to "slurp up" a comprehensive knowledge base from the entire unstructured Web. Some argue that some kind of (currently-undiscovered) conceptually straightforward, but mathematically difficult, "Master Algorithm" could lead to AGI. Finally, a few "emergent" approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[152][153]

Many of the problems in this article may also require general intelligence, if machines are to solve the problems as well as people do. For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence). A problem like machine translation is considered "AI-complete", because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

No established unifying theory or paradigm guides AI research. Researchers disagree about many issues.[154] A few of the most long standing questions that have remained unanswered are these: should artificial intelligence simulate natural intelligence by studying psychology or neurobiology? Or is human biology as irrelevant to AI research as bird biology is to aeronautical engineering?[17]Can intelligent behavior be described using simple, elegant principles (such as logic or optimization)? Or does it necessarily require solving a large number of unrelated problems?[18]

In the 1940s and 1950s, a number of researchers explored the connection between neurobiology, information theory, and cybernetics. Some of them built machines that used electronic networks to exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many of these researchers gathered for meetings of the Teleological Society at Princeton University and the Ratio Club in England.[155] By 1960, this approach was largely abandoned, although elements of it would be revived in the 1980s.

When access to digital computers became possible in the mid-1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation. The research was centered in three institutions: Carnegie Mellon University, Stanford and MIT, and as described below, each one developed its own style of research. John Haugeland named these symbolic approaches to AI "good old fashioned AI" or "GOFAI".[156] During the 1960s, symbolic approaches had achieved great success at simulating high-level "thinking" in small demonstration programs. Approaches based on cybernetics or artificial neural networks were abandoned or pushed into the background.[157]Researchers in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science. Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon University would eventually culminate in the development of the Soar architecture in the middle 1980s.[158][159]

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.[17] His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[160] Logic was also the focus of the work at the University of Edinburgh and elsewhere in Europe which led to the development of the programming language Prolog and the science of logic programming.[161]

Researchers at MIT (such as Marvin Minsky and Seymour Papert)[162] found that solving difficult problems in vision and natural language processing required ad-hoc solutionsthey argued that no simple and general principle (like logic) would capture all the aspects of intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as opposed to the "neat" paradigms at CMU and Stanford).[18] Commonsense knowledge bases (such as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one complicated concept at a time.[163]

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[164] This "knowledge revolution" led to the development and deployment of expert systems (introduced by Edward Feigenbaum), the first truly successful form of AI software.[42] A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules that illustrate AI.[165] The knowledge revolution was also driven by the realization that enormous amounts of knowledge would be required by many simple AI applications.

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition. A number of researchers began to look into "sub-symbolic" approaches to specific AI problems.[19] Sub-symbolic methods manage to approach intelligence without specific representations of knowledge.

This includes embodied, situated, behavior-based, and nouvelle AI. Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI and focused on the basic engineering problems that would allow robots to move and survive.[166] Their work revived the non-symbolic point of view of the early cybernetics researchers of the 1950s and reintroduced the use of control theory in AI. This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[167][168]

Interest in neural networks and "connectionism" was revived by David Rumelhart and others in the middle of the 1980s.[171] Artificial neural networks are an example of soft computingthey are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient. Other soft computing approaches to AI include fuzzy systems, Grey system theory, evolutionary computation and many statistical tools. The application of soft computing to AI is studied collectively by the emerging discipline of computational intelligence.[172]

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results. However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures. The shared mathematical language permitted a high level of collaboration with more established fields (like mathematics, economics or operations research).[d] Compared with GOFAI, new "statistical learning" techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets. The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models; AI research was becoming more scientific. Nowadays results of experiments are often rigorously measurable, and are sometimes (with difficulty) reproducible.[44][173] Different statistical learning techniques have different limitations; for example, basic HMM cannot model the infinite possible combinations of natural language. Critics note that the shift from GOFAI to statistical learning is often also a shift away from explainable AI. In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.

AI has developed many tools to solve the most difficult problems in computer science. A few of the most general of these methods are discussed below.

Many problems in AI can be solved theoretically by intelligently searching through many possible solutions:[183] Reasoning can be reduced to performing a search. For example, logical proof can be viewed as searching for a path that leads from premises to conclusions, where each step is the application of an inference rule.[184] Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[185] Robotics algorithms for moving limbs and grasping objects use local searches in configuration space.[128] Many learning algorithms use search algorithms based on optimization.

Simple exhaustive searches[186] are rarely sufficient for most real-world problems: the search space (the number of places to search) quickly grows to astronomical numbers. The result is a search that is too slow or never completes. The solution, for many problems, is to use "heuristics" or "rules of thumb" that prioritize choices in favor of those more more likely to reach a goal and to do so in a shorter number of steps. In some search methodologies heuristics can also serve to entirely eliminate some choices unlikely to lead to a goal (called "pruning the search tree"). Heuristics supply the program with a "best guess" for the path on which the solution lies.[187] Heuristics limit the search for solutions into a smaller sample size.

A very different kind of search came to prominence in the 1990s, based on the mathematical theory of optimization. For many problems, it is possible to begin the search with some form of a guess and then refine the guess incrementally until no more refinements can be made. These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms are simulated annealing, beam search and random optimization.[188]

Evolutionary computation uses a form of optimization search. For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses). Classic evolutionary algorithms include genetic algorithms, gene expression programming, and genetic programming.[189] Alternatively, distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).[190][191]

Logic[192] is used for knowledge representation and problem solving, but it can be applied to other problems as well. For example, the satplan algorithm uses logic for planning[193] and inductive logic programming is a method for learning.[194]

Several different forms of logic are used in AI research. Propositional logic[195] involves truth functions such as "or" and "not". First-order logic[196] adds quantifiers and predicates, and can express facts about objects, their properties, and their relations with each other. Fuzzy set theory assigns a "degree of truth" (between 0 and 1) to vague statements such as "Alice is old" (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false. Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as "if you are close to the destination station and moving fast, increase the train's brake pressure"; these vague rules can then be numerically refined within the system. Fuzzy logic fails to scale well in knowledge bases; many AI researchers question the validity of chaining fuzzy-logic inferences.[e][198][199]

Default logics, non-monotonic logics and circumscription[103] are forms of logic designed to help with default reasoning and the qualification problem. Several extensions of logic have been designed to handle specific domains of knowledge, such as: description logics;[91] situation calculus, event calculus and fluent calculus (for representing events and time);[92] causal calculus;[93] belief calculus (belief revision);[200] and modal logics.[94] Logics to model contradictory or inconsistent statements arising in multi-agent systems have also been designed, such as paraconsistent logics.

Many problems in AI (in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of powerful tools to solve these problems using methods from probability theory and economics.[201]

Bayesian networks[202] are a very general tool that can be used for various problems: reasoning (using the Bayesian inference algorithm),[203] learning (using the expectation-maximization algorithm),[f][205] planning (using decision networks)[206] and perception (using dynamic Bayesian networks).[207] Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[207] Compared with symbolic logic, formal Bayesian inference is computationally expensive. For inference to be tractable, most observations must be conditionally independent of one another. Complicated graphs with diamonds or other "loops" (undirected cycles) can require a sophisticated method such as Markov chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities. Bayesian networks are used on Xbox Live to rate and match players; wins and losses are "evidence" of how good a player is[citation needed]. AdSense uses a Bayesian network with over 300 million edges to learn which ads to serve.

A key concept from the science of economics is "utility": a measure of how valuable something is to an intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using decision theory, decision analysis,[209] and information value theory.[109] These tools include models such as Markov decision processes,[210] dynamic decision networks,[207] game theory and mechanism design.[211]

The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and controllers ("if shiny then pick up"). Controllers do, however, also classify conditions before inferring actions, and therefore classification forms a central part of many AI systems. Classifiers are functions that use pattern matching to determine a closest match. They can be tuned according to examples, making them very attractive for use in AI. These examples are known as observations or patterns. In supervised learning, each pattern belongs to a certain predefined class. A class is a decision that has to be made. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.[212]

A classifier can be trained in various ways; there are many statistical and machine learning approaches. The decision tree[213] is perhaps the most widely used machine learning algorithm. Other widely used classifiers are the neural network,[215]k-nearest neighbor algorithm,[g][217]kernel methods such as the support vector machine (SVM),[h][219]Gaussian mixture model,[220] and the extremely popular naive Bayes classifier.[i][222] Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise. Model-based classifiers perform well if the assumed model is an extremely good fit for the actual data. Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as "naive Bayes" on most practical data sets.[223]

Neural networks were inspired by the architecture of neurons in the human brain. A simple "neuron" N accepts input from other neurons, each of which, when activated (or "fired"), casts a weighted "vote" for or against whether neuron N should itself activate. Learning requires an algorithm to adjust these weights based on the training data; one simple algorithm (dubbed "fire together, wire together") is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another. The neural network forms "concepts" that are distributed among a subnetwork of shared[j] neurons that tend to fire together; a concept meaning "leg" might be coupled with a subnetwork meaning "foot" that includes the sound for "foot". Neurons have a continuous spectrum of activation; in addition, neurons can process inputs in a nonlinear way rather than weighing straightforward votes. Modern neural networks can learn both continuous functions and, surprisingly, digital logical operations. Neural networks' early successes included predicting the stock market and (in 1995) a mostly self-driving car.[k] In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending; for example, AI-related M&A in 2017 was over 25 times as large as in 2015.[226][227]

The study of non-learning artificial neural networks[215] began in the decade before the field of AI research was founded, in the work of Walter Pitts and Warren McCullouch. Frank Rosenblatt invented the perceptron, a learning network with a single layer, similar to the old concept of linear regression. Early pioneers also include Alexey Grigorevich Ivakhnenko, Teuvo Kohonen, Stephen Grossberg, Kunihiko Fukushima, Christoph von der Malsburg, David Willshaw, Shun-Ichi Amari, Bernard Widrow, John Hopfield, Eduardo R. Caianiello, and others[citation needed].

The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events). Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks.[228] Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ("fire together, wire together"), GMDH or competitive learning.[229]

Today, neural networks are often trained by the backpropagation algorithm, which has been around since 1970 as the reverse mode of automatic differentiation published by Seppo Linnainmaa,[230][231] and was introduced to neural networks by Paul Werbos.[232][233][234]

Hierarchical temporal memory is an approach that models some of the structural and algorithmic properties of the neocortex.[235]

To summarize, most neural networks use some form of gradient descent on a hand-created neural topology. However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches[citation needed]. One advantage of neuroevolution is that it may be less prone to get caught in "dead ends".[236]

Deep learning is the use of artificial neural networks which have several layers of neurons between the network's inputs and outputs. Deep learning has transformed many important subfields of artificial intelligence[why?], including computer vision, speech recognition, natural language processing and others.[237][238][239]

According to one overview,[240] the expression "Deep Learning" was introduced to the machine learning community by Rina Dechter in 1986[241] and gained traction afterIgor Aizenberg and colleagues introduced it to artificial neural networks in 2000.[242] The first functional Deep Learning networks were published by Alexey Grigorevich Ivakhnenko and V. G. Lapa in 1965.[243][pageneeded] These networks are trained one layer at a time. Ivakhnenko's 1971 paper[244] describes the learning of a deep feedforward multilayer perceptron with eight layers, already much deeper than many later networks. In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning. Similar to shallow artificial neural networks, deep neural networks can model complex non-linear relationships.

Deep learning often uses convolutional neural networks (CNNs), whose origins can be traced back to the Neocognitron introduced by Kunihiko Fukushima in 1980.[246] In 1989, Yann LeCun and colleagues applied backpropagation to such an architecture. In the early 2000s, in an industrial application, CNNs already processed an estimated 10% to 20% of all the checks written in the US.[247]Since 2011, fast implementations of CNNs on GPUs havewon many visual pattern recognition competitions.[239]

CNNs with 12 convolutional layers were used with reinforcement learning by Deepmind's "AlphaGo Lee", the program that beat a top Go champion in 2016.[248]

Early on, deep learning was also applied to sequence learning with recurrent neural networks (RNNs)[249] which are theoretically Turing complete[250] and can run arbitrary programs to process arbitrary sequences of inputs. The depth of an RNN is unlimited and depends on the length of its input sequence; thus, an RNN is an example of deep learning.[239] RNNs can be trained by gradient descent[251][252][253] but suffer from the vanishing gradient problem.[237][254] In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[255]

Numerous researchers now use variants of a deep learning recurrent NN called the long short-term memory (LSTM) network published by Hochreiter & Schmidhuber in 1997.[256] LSTM is often trained by Connectionist Temporal Classification (CTC).[257] At Google, Microsoft and Baidu this approach has revolutionized speech recognition.[258][259][260] For example, in 2015, Google's speech recognition experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to billions of smartphone users.[261] Google also used LSTM to improve machine translation,[262] Language Modeling[263] and Multilingual Language Processing.[264] LSTM combined with CNNs also improved automatic image captioning[265] and a plethora of other applications.

AI, like electricity or the steam engine, is a general purpose technology. There is no consensus on how to characterize which tasks AI tends to excel at.[266] While projects such as AlphaZero have succeeded in generating their own knowledge from scratch, many other machine learning projects require large training datasets.[267][268] Researcher Andrew Ng has suggested, as a "highly imperfect rule of thumb", that "almost anything a typical human can do with less than one second of mental thought, we can probably now or in the near future automate using AI."[269] Moravec's paradox suggests that AI lags humans at many tasks that the human brain has specifically evolved to perform well.[134]

Games provide a well-publicized benchmark for assessing rates of progress. AlphaGo around 2016 brought the era of classical board-game benchmarks to a close. Games of imperfect knowledge provide new challenges to AI in game theory.[270][271] E-sports such as StarCraft continue to provide additional public benchmarks.[272][273] Many competitions and prizes, such as the Imagenet Challenge, promote research in artificial intelligence. The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[274]

The "imitation game" (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.[275] A derivative of the Turing test is the Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA). As the name implies, this helps to determine that a user is an actual person and not a computer posing as a human. Unlike the standard Turing test, CAPTCHA is administered by a machine and targeted to a human as opposed to being administered by a human and targeted to a machine. A computer asks a user to complete a simple test then generates a grade for that test. Computers are unable to solve the problem, so correct solutions are deemed to be the result of a person taking the test. A common type of CAPTCHA is the test that requires the typing of distorted letters, numbers or symbols that appear in an image undecipherable by a computer.

Proposed "universal intelligence" tests aim to compare how well machines, humans, and even non-human animals perform on problem sets that are generic as possible. At an extreme, the test suite can contain every possible problem, weighted by Kolmogorov complexity; unfortunately, these problem sets tend to be dominated by impoverished pattern-matching exercises where a tuned AI can easily exceed human performance levels.[277][278]

Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[279] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[280] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[281][282]

AI is relevant to any intellectual task. Modern artificial intelligence techniques are pervasive[284] and are too numerous to list here. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect.

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,[286] prediction of judicial decisions,[287] targeting online advertisements, [288][289] and energy storage[290]

With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution,[291] major publishers now use artificial intelligence (AI) technology to post stories more effectively and generate higher volumes of traffic.[292]

AI can also produce Deepfakes, a content-altering technology. ZDNet reports, "It presents something that did not actually occur," Though 88% of Americans believe Deepfakes can cause more harm than good, only 47% of them believe they can be targeted. The boom of election year also opens public discourse to threats of videos of falsified politician media.[293]

AI in healthcare is often used for classification, whether to automate initial evaluation of a CT scan or EKG or to identify high-risk patients for population health. The breadth of applications is rapidly increasing.As an example, AI is being applied to the high-cost problem of dosage issueswhere findings suggested that AI could save $16 billion. In 2016, a groundbreaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.[294]

Artificial intelligence is assisting doctors. According to Bloomberg Technology, Microsoft has developed AI to help doctors find the right treatments for cancer.[295] There is a great amount of research and drugs developed relating to cancer. In detail, there are more than 800 medicines and vaccines to treat cancer. This negatively affects the doctors, because there are too many options to choose from, making it more difficult to choose the right drugs for the patients. Microsoft is working on a project to develop a machine called "Hanover"[citation needed]. Its goal is to memorize all the papers necessary to cancer and help predict which combinations of drugs will be most effective for each patient. One project that is being worked on at the moment is fighting myeloid leukemia, a fatal cancer where the treatment has not improved in decades. Another study was reported to have found that artificial intelligence was as good as trained doctors in identifying skin cancers.[296] Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[297] One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.[298]

According to CNN, a recent study by surgeons at the Children's National Medical Center in Washington successfully demonstrated surgery with an autonomous robot. The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.[299] IBM has created its own artificial intelligence computer, the IBM Watson, which has beaten human intelligence (at some levels). Watson has struggled to achieve success and adoption in healthcare.[300]

Advancements in AI have contributed to the growth of the automotive industry through the creation and evolution of self-driving vehicles. As of 2016[update], there are over 30 companies utilizing AI into the creation of self-driving cars. A few companies involved with AI include Tesla, Google, and Apple.[301]

Many components contribute to the functioning of self-driving cars. These vehicles incorporate systems such as braking, lane changing, collision prevention, navigation and mapping. Together, these systems, as well as high-performance computers, are integrated into one complex vehicle.[302]

Recent developments in autonomous automobiles have made the innovation of self-driving trucks possible, though they are still in the testing phase. The UK government has passed legislation to begin testing of self-driving truck platoons in 2018.[303] Self-driving truck platoons are a fleet of self-driving trucks following the lead of one non-self-driving truck, so the truck platoons aren't entirely autonomous yet. Meanwhile, the Daimler, a German automobile corporation, is testing the Freightliner Inspiration which is a semi-autonomous truck that will only be used on the highway.[304]

One main factor that influences the ability for a driverless automobile to function is mapping. In general, the vehicle would be pre-programmed with a map of the area being driven. This map would include data on the approximations of street light and curb heights in order for the vehicle to be aware of its surroundings. However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[305] Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[306]

Another factor that is influencing the ability of a driverless automobile is the safety of the passenger. To make a driverless automobile, engineers must program it to handle high-risk situations. These situations could include a head-on collision with pedestrians. The car's main goal should be to make a decision that would avoid hitting the pedestrians and saving the passengers in the car. But there is a possibility the car would need to make a decision that would put someone in danger. In other words, the car would need to decide to save the pedestrians or the passengers.[307] The programming of the car in these situations is crucial to a successful driverless automobile.

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation. The use of AI in banking can be traced back to 1987 when Security Pacific National Bank in the US set-up a Fraud Prevention Task force to counter the unauthorized use of debit cards.[308] Programs like Kasisto and Moneystream are using AI in financial services.

Banks use artificial intelligence systems today to organize operations, maintain book-keeping, invest in stocks, and manage properties. AI can react to changes overnight or when business is not taking place.[309] In August 2001, robots beat humans in a simulated financial trading competition.[310] AI has also reduced fraud and financial crimes by monitoring behavioral patterns of users for any abnormal changes or anomalies.[311][312][313]

AI is increasingly being used by corporations. Jack Ma has controversially predicted that AI CEO's are 30 years away.[314][315]

The use of AI machines in the market in applications such as online trading and decision making has changed major economic theories.[316] For example, AI-based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing. Furthermore, AI machines reduce information asymmetry in the market and thus making markets more efficient while reducing the volume of trades[citation needed]. Furthermore, AI in the markets limits the consequences of behavior in the markets again making markets more efficient[citation needed]. Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking[citation needed].. In August 2019, the AICPA introduced an AI training course for accounting professionals.[317]

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Artificial intelligence - Wikipedia

The Global Automotive Artificial Intelligence Market is expected to grow from USD 715.71 Million in 2019 to USD 3,967.57 Million by the end of 2025 at…

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Market Segmentation & Coverage: This research report categorizes the Automotive Artificial Intelligence to forecast the revenues and analyze the trends in each of the following sub-markets:

New York, July 15, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Automotive Artificial Intelligence Market Research Report by Technology, by Process, by Offerings, by Application - Global Forecast to 2025 - Cumulative Impact of COVID-19" - https://www.reportlinker.com/p05913345/?utm_source=GNW

On the basis of Technology, the Automotive Artificial Intelligence Market is studied across Computer Vision, Context Awareness, Deep Learning, Machine Learning, and Natural Language Processing.

On the basis of Process, the Automotive Artificial Intelligence Market is studied across Data Mining, Image Recognition, and Signal Recognition.

On the basis of Offerings, the Automotive Artificial Intelligence Market is studied across Hardware and Software. The Hardware further studied across Neuromorphic Architecture and Von Neumann Architecture. The Software further studied across Platforms and Solutions.

On the basis of Application, the Automotive Artificial Intelligence Market is studied across Autonomous Vehicle, HumanMachine Interface, and Semi-Autonomous Driving.

On the basis of Geography, the Automotive Artificial Intelligence Market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas region is studied across Argentina, Brazil, Canada, Mexico, and United States. The Asia-Pacific region is studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, South Korea, and Thailand. The Europe, Middle East & Africa region is studied across France, Germany, Italy, Netherlands, Qatar, Russia, Saudi Arabia, South Africa, Spain, United Arab Emirates, and United Kingdom.

Company Usability Profiles:The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Automotive Artificial Intelligence Market including Alphabet Inc., Audi AG, Bayerische Motoren Werke AG, Ford Motor Company, General Motors Company, Harman International Industries, Inc., Intel Corporation, International Business Machines Corporation, Microsoft Corporation, NVIDIA Corporation, Qualcomm Inc., Tesla, Inc., Toyota Motor Corporation, Volvo Car Corporation, and Xilinx Inc..

FPNV Positioning Matrix:The FPNV Positioning Matrix evaluates and categorizes the vendors in the Automotive Artificial Intelligence Market on the basis of 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.

Competitive Strategic Window:The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines 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.

Cumulative Impact of COVID-19:COVID-19 is an incomparable global public health emergency that has affected almost every industry, so for and, the long-term effects projected to impact the industry growth during the forecast period. Our ongoing research amplifies our research framework to ensure the inclusion of underlaying COVID-19 issues and potential paths forward. The report is delivering insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The updated study provides insights, analysis, estimations, and forecast, considering the COVID-19 impact on the market.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyzes 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, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments

The report answers questions such as:1. What is the market size and forecast of the Global Automotive Artificial Intelligence Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Automotive Artificial Intelligence Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Automotive Artificial Intelligence Market?4. What is the competitive strategic window for opportunities in the Global Automotive Artificial Intelligence Market?5. What are the technology trends and regulatory frameworks in the Global Automotive Artificial Intelligence Market?6. What are the modes and strategic moves considered suitable for entering the Global Automotive Artificial Intelligence Market?Read the full report: https://www.reportlinker.com/p05913345/?utm_source=GNW

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The Global Automotive Artificial Intelligence Market is expected to grow from USD 715.71 Million in 2019 to USD 3,967.57 Million by the end of 2025 at...

Infographic: Artificial Intelligence From World Domination To Inclusive Education – Feminism in India

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Technology, in a short time, has transformed the relationship between human and their environment, with each other, and the society at large. Artificial Intelligence (hereby referred to as AI)has found its way in many spheres of our lives. AI finds itself in smartphones, entertainment platforms, hospitals, transportation and outer space, among a plethora of other spaces.

While fears of technology replacing human beings in various roles and occupations loom large, AI provides considerable scope for various domains.

Artificial intelligence isthe ability of computer systems of simulating human intelligence processes and do complex self-corrective tasks.The goal is to create machines and systems which when interacting with the given environment, act accordingly upon the received data in a way that can be considered intelligent.

AI is slowly finding its way everywhere in our lives such as personal assistants like Alexa, face ids, personalised social media feeds and ads, banking, and even Google Maps. AI plays a part in various things says Kshitij from Pixxel, like agriculture, space, everyday things like automatically adjusting your screen brightness, customising ads on Instagram and more.

It is often claimed that AI is still in its primary stage. On the other hand, many would state thatAI would in the near future, gain complete control over humanity. According to the variousprophecies and forecasts, Artificial Intelligence would control the reigns of intelligence, andoutsmart and outperform humans beings at most things considered important yet relativelysimpler tasks, like driving or generating sentences, and so forth.

However, human involvement would remain key in all tasks performed by the AI. The degree of human involvement is that of contestation, as to what tasks and duties are considered important or not important enough to be transferred to Artificial Intelligence.

It is important to see here that AI is performing primarily assistive functions to human beings in a variety of ways. At the same time, humans are always required and involved in creating such systems and ensuring their smooth functioning, so rising up against humanity wont happen in the near future!

AI can work remotely, anonymously and in a personalised fashion. The coronavirus pandemic has proven that working remotely is a possible reality, AI can aid these processes as people do not need to be physically present and can be reached out to even in areas which seem inaccessible. AI can protect the identities of individuals and does not hold the same prejudices that humans do to discriminate against them at the various process. Apps powered by AI have provided aid with mental health, help in translation, and aid accessibility for people with disabilities.

The personalised way in which AI can operate can help teachers track the individual progress of students. This can be done anonymously to avoid the possibility of biases. AI can create individualised ways according to personalised needs of the students. This would not only help the student with individual attention but also assist the teachers with their work. AI can also help with captioning, image description, and language comprehension according to the needs of the students. Since AI can work remotely, it can reach areas considered inaccessible or even those with conflict.

Also read: How Unbiased Is Artificial Intelligence?

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Infographic: Artificial Intelligence From World Domination To Inclusive Education - Feminism in India

The top 16 companies using artificial intelligence to revolutionize drug discovery, according to experts – Business Insider India

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Artificial intelligence is poised to dramatically overhaul how pharmaceutical giants like Bayer, Pfizer, and GlaxoSmithKline pinpoint innovative and potentially lucrative new drugs.

The technology is under the spotlight now, as top companies and federal agencies try to use it to quickly find a vaccine or treatment for COVID-19. But the increase in partnerships between drug manufacturers and AI-powered startups could have much broader ramifications for the drug discovery process.

It currently takes upwards of a decade and billions of dollars to bring a new treatment to market including five or more years of testing just to discover promising leads. Artificial intelligence can help cut that initial research period by as much as 50%, according to some experts.

Industry titans are rushing to link-up with promising startups that can help shave time and money off of the process. Swiss drug-giant Roche, for example, has ongoing deals with French data-science firm Owkin, among others, and bought the cancer research startup Flatiron Health in 2018.

The AI drug discovery market is expected to swell to $1.4 billion by 2024 and the number of startups vying for their piece has grown too. In 2014, there were an estimated 89 AI-driven companies focused on drug discovery. Now, there are as many as 217.

"There's been quite good investment within this area," Amol Kotwal, senior director at consulting firm Frost & Sullivan, told Business Insider. "There's a lot of innovative partnerships with big pharma. And they're seeing the results, which is now reinforcing that you can really cut time."

Frost & Sullivan recently selected the top 16 firms revolutionizing research into new treatments, basing its selection on a number of factors, including ongoing deals with pharmaceutical giants, fundraising to-date, and how successful each has been in helping to advance promising drugs to human testing.

While the list doesn't include every hot AI health company for instance, Insitro, which has raised significant funding and scored multi-million partnerships, didn't make the cut Kotwal says the startups chosen were the ones with the most promising drugs in clinical development,

"They have the technology, they're generating data, but they still do not have any molecules with the partner companies or in their own pipeline," he said of Insitro.

Business Insider compiled the firm's choices including fundraising estimates from PitchBook when the company declined or did not respond to requests to provide to highlight the key players in the industry:

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The top 16 companies using artificial intelligence to revolutionize drug discovery, according to experts - Business Insider India

Hardbacon secures funding to develop artificial intelligence capable of predicting changes in the stock market – PRNewswire

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MONTREAL, July 14, 2020 /PRNewswire/ --Hardbacon is pleased to announce that it will receive consulting services and has obtained conditional funding of $50,000 for an artificial intelligence research and development project to predict stock prices. The grant is part of the National Research Council of Canada's Industrial Research Assistance Program (NRC IRAP).

Hardbacon, a mobile budgeting and investment tracking app, is currently developing a stock rating system, which will leverage artificial intelligence to help investors pick stocks.

Ratings generated by artificial intelligence will appear in Hardbacon's mobile application, and will also be made available under license to financial institutions wishing to use these ratings or to offer them to their customers.

"Many Hardbacon users asked us to tell them what to invest in", explained Julien Brault, CEO of Hardbacon. Until now we had refused, until one of our employees presented us with a promising academic article that he had written about the possibility of using artificial intelligence to generate predictive ratings. We are grateful that the NRC IRAP has agreed to support this project."

For more information, contact:

Julien Brault, CEO of Hardbacon; 514-250-3255; [emailprotected]

To learn more about Hardbacon, visit our website : https://hardbacon.ca/

Disclaimer:The news site hosting this press release is not associated with Hardbacon or Bacon Financial Technologies Inc. It is merely publishing a press release announcement submitted by a company, without any stated or implied endorsement of the information, product or service. Please check with a Registered Investment Adviser or Certified Financial Planner before making any investment.

About Hardbacon

Hardbacon strives to help Canadians make better financial decisions. The company, which obtained $1.1 million in funding, markets a mobile application that enables subscribers to create a plan, a budget and to analyze their investments. The mobile app, available in the App Store and Google Play, can link to bank and investment accounts for more than 100 Canadian financial institutions.

Press Contact:

Julien Brault 5142503255 https://hardbacon.ca/

SOURCE Hardbacon

https://hardbacon.ca

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Hardbacon secures funding to develop artificial intelligence capable of predicting changes in the stock market - PRNewswire

Cloud, Artificial Intelligence (AI), and 5G are already reshaping the Oil and Gas Industry Huawei drives the intelligent transformation of the sector…

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Today, the Huawei Oil & Gas Virtual Summit 2020 (www.Huawei.com) exploring 'Data to Barrel' was successfully hosted online. The summit gathered together global customers, industry partners, and thought leaders including representatives from the Abu Dhabi National Oil Company (ADNOC), Schlumberger SIS, and the former Chief Information Officer (CIO) of French giant TOTAL to share their experiences of helping oil and gas companies increase profits while cutting costs, creating added value through digital transformation. Key suggestions on how the industry can overcome challenges at this particular point in time, adapting to the new normal of the pandemic and post-pandemic periods, were also fully explored.

The Oil and Gas Industry Faces Upheaval: Huawei is Positioned to Help

In the first half of 2020, due to the global economic downturn amid the spread of COVID-19, international oil prices fell to a low of 30 dollars per barrel. In May, West Texas Intermediate (WTI) crude oil futures prices even turned negative, a historically unprecedented event. Undoubtedly, the oil and gas industry has entered an extremely difficult period and is witnessing changes, the likes of which have not been seen for over a century.

Huawei has been working hard to help oil and gas customers cope with these current challenges. David Sun, Vice President of Huawei's Enterprise Business Group and Director of the Global Energy Business Department, noted that, over the past decade, Huawei has partnered with customers in the oil and gas industry and together witnessed oil prices peak at 120 dollars per barrel, as well as fall to that low of 30 dollars. Along the way, Huawei's role has changed and upgraded with the support and help of oil and gas companies. Evolving from a vendor that simply provided switches, routers, and network devices, to becoming a full partner dedicated to providing digital transformation solutions, Huawei works with partners and customers alike to jointly promote the application of 5G, Artificial Intelligence (AI), and big data in the oil and gas industry. It continues to explore new technologies and applications, where solutions to the current challenges lie.

Indeed, using elastic computing, big data analytics, AI, and cloud data centers, Huawei has already helped oil and gas customers achieve digital transformation, promoting the construction of intelligent oilfields and increasing oil and gas reserves.

Working with partners, Huawei planned and built a computing AI platform for an industry customer, to implement AI training and big data analytics. This has, in turn, led to an increase in both oil and gas reserves and in production. Indeed, solutions have been implemented in various scenarios, including artificial-lift fault diagnosis, well-logging and reservoir identification, and seismic first arrival wave identification, extracting significant value from underutilized formerly 'useless' data.

In the words of Dr. Mohamed Akoum from ADNOC: In an era of change for industries around the world, ADNOC continues to drive innovation and embed advanced technologies across its value chain to optimize performance, boost profitability and build resilience.

New ICT Technologies Reshape the Oil and Gas Industry: Huawei Offers a Wealth of Experience

Today, 150 years after the first successful extraction of oil from a drilled well, accessible underground oil resources have been all but exhausted. Oil companies, by necessity, are therefore now exploring deep-water, pre-salt, and unconventional reservoirs.

At 60 years old, Daqing Oilfield the largest oilfield in China, situated in Heilongjiang, the country's northernmost province has faced enormous challenges in terms of reserve replacement, stable production pressure, cost reductions, and efficiency improvements.

At the Huawei Oil & Gas Virtual Summit 2020, Zhang Tiegang, former Deputy Chief Engineer of the Exploration and Development Research Institute at Daqing Oilfield, explained that seismic exploration technologies to detect oil and gas reserves have been the method of choice for most oil companies. Increasing seismic exploration while decreasing well drilling, he noted, has become a new measure widely used in the industry. However, high precision and massive data processing have brought their own challenges to seismic exploration and oilfield exploration and development. With a single seismic exploration work area now expanded to over 2000 square kilometers, the volume of data collected through the broadband, wide-azimuth, and high-density seismic data collection technology has exceeded 1 TB per square kilometer.

To help Daqing Oilfield address these issues, Huawei built a dedicated oil and gas exploration cloud. The cloud data center improves computing power by eight times and has similarly improved prestack seismic data processing capability by five times, from 400 square kilometers to 2000 square kilometers, matching work area requirements. Elsewhere, AI and big data capabilities have been used to re-analyze 10 PB of the customer's historical exploration data, to mine new value from it and support extraction decision-making, bringing huge additional value to the oilfield.

Huawei is empowering a wide range of industries through 5G networking. In the oil and gas industry, 5G technologies are changing the operation modes of seismic data collection. Huawei has put 5G network features to work including high bandwidth, wide connectivity, and low latency to help achieve high-speed backhaul of seismic data, reducing the manual cabling workload and significantly improving the efficiency of seismic data collection.

Elsewhere, Huawei 5G networks are already being used in oilfields and stations to support robot inspection, drone inspection, and Augmented Reality (AR) and Virtual Reality (VR) applications.

Additionally, the Huawei Horizon Digital Platform helps oil and gas customers break down legacy siloed service systems and quickly release service applications as micro-services, to meet the complex and changing needs of the industry. For example, Huawei has deployed an enterprise cloud for SONATRACH, the national state-owned oil company of Algeria. The cloud-based solution manages and coordinates multiple data centers, eliminates resource silos, and greatly improves overall operation efficiency.

As a global ICT solutions provider, Huawei is committed to bringing digital to every oil and gas company. At the Huawei Oil & Gas Virtual Summit 2020, Wang Hao, Chief Technology Officer (CTO) of the Oil & Gas Development Department for Huawei's Enterprise Business Group, said that Huawei will use ICT as a new engine to work even more closely with industry customers in challenging times. Indeed, Huawei is already working with 19 of the top 30 oil and gas companies, in 45 countries and regions around the world, helping them achieve digital transformation. Ultimately, this will bring more benefits to the upstream, more security to the midstream, and more value to the downstream.

Such innovative ICT technologies AI, cloud, edge computing, and 5G will reshape the oil and gas industry. As David Sun concluded at the Huawei Oil & Gas Virtual Summit 2020: According to IDC's latest survey, Chinese industrial users see Huawei as the digital transformation leader, ranking number one. In the future, we hope to share Huawei's digital transformation capabilities and experiences in China's oil and gas industry with global customers, to help achieve ever greater business success.

About Huawei:Huawei (www.Huawei.com) is a leading global provider of information and communications technology (ICT) infrastructure and smart devices. With integrated solutions across four key domains telecom networks, IT, smart devices, and cloud services we are committed to bringing digital to every person, home and organization for a fully connected, intelligent world.

Huawei's end-to-end portfolio of products, solutions and services are both competitive and secure. Through open collaboration with ecosystem partners, we create lasting value for our customers, working to empower people, enrich home life, and inspire innovation in organizations of all shapes and sizes.

At Huawei, innovation focuses on customer needs. We invest heavily in basic research, concentrating on technological breakthroughs that drive the world forward. We have more than 194,000 employees by the end of 2019, and we operate in more than 170 countries and regions. Founded in 1987, Huawei is a private company fully owned by its employees.

For more information, please visit Huawei online at http://www.Huawei.com.

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Cloud, Artificial Intelligence (AI), and 5G are already reshaping the Oil and Gas Industry Huawei drives the intelligent transformation of the sector...

The path to real-world artificial intelligence – TechRepublic

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Experts from MIT and IBM held a webinar this week to discuss where AI technologies are today and advances that will help make their usage more practical and widespread.

Image: Sompong Rattanakunchon / Getty Images

Artificial intelligence has made significant strides in recent years, but modern AI techniques remain limited, a panel of MIT professors and the director of the MIT-IBM Watson AI Lab said during a webinar this week.

Neural networks can perform specific, well-defined tasks but they struggle in real-world situations that go beyond pattern recognition and present obstacles like limited data, reliance on self-training, and answering questions like "why" and "how" versus "what," the panel said.

The future of AI depends on enabling AI systems to do something once considered impossible: Learn by demonstrating flexibility, some semblance of reasoning, and/or by transferring knowledge from one set of tasks to another, the group said.

SEE: Robotic process automation: A cheat sheet (free PDF) (TechRepublic)

The panel discussion was moderated by David Schubmehl, a research director at IDC, and it began with a question he posed asking about the current limitations of AI and machine learning.

"The striking success right now in particular, in machine learning, is in problems that require interpretation of signalsimages, speech and language," said panelist Leslie Kaelbling, a computer science and engineering professor at MIT.

For years, people have tried to solve problems like detecting faces and images and directly engineering solutions that didn't work, she said.

We have become good at engineering algorithms that take data and use that to derive a solution, she said. "That's been an amazing success." But it takes a lot of data and a lot of computation so for some problems formulations aren't available yet that would let us learn from the amount of data available, Kaelbling said.

SEE:9 super-smart problem solvers take on bias in AI, microplastics, and language lessons for chatbots(TechRepublic)

One of her areas of focus is in robotics, and it's harder to get training examples there because robots are expensive and parts break, "so we really have to be able to learn from smaller amounts of data," Kaelbling said.

Neural networks and deep learning are the "latest and greatest way to frame those sorts of problems and the successes are many," added Josh Tenenbaum, a professor of cognitive science and computation at MIT.

But when talking about general intelligence and how to get machines to understand the world there is still a huge gap, he said.

"But on the research side really exciting things are starting to happen to try to capture some steps to more general forms of intelligence [in] machines," he said. In his work, "we're seeing ways in which we can draw insights from how humans understand the world and taking small steps to put them in machines."

Although people think of AI as being synonymous with automation, it is incredibly labor intensive in a way that doesn't work for most of the problems we want to solve, noted David Cox, IBM director of the MIT-IBM Watson AI Lab.

Echoing Kaelbling, Cox said that leveraging tools today like deep learning requires huge amounts of "carefully curated, bias-balanced data," to be able to use them well. Additionally, for most problems we are trying to solve, we don't have those "giant rivers of data" to build a dam in front of to extract some value from that river, Cox said.

Today, companies are more focused on solving some type of one-off problem and even when they have big data, it's rarely curated, he said. "So most of the problems we love to solve with AIwe don't have the right tools for that."

That's because we have problems with bias and interpretability with humans using these tools and they have to understand why they are making these decisions, Cox said. "They're all barriers."

However, he said, there's enormous opportunity looking at all these different fields to chart a path forward.

That includes using deep learning, which is good for pattern recognition, to help solve difficult search problems, Tenenbaum said.To develop intelligent agents, scientists need to use all the available tools, said Kaelbling. For example, neural networks are needed for perception as well as higher level and more abstract types of reasoning to decide, for example, what to make for dinner or to decide how to disperse supplies.

"The critical thing technologically is to realize the sweet spot for each piece and figure out what it is good at and not good at. Scientists need to understand the role each piece plays," she said.

The MIT and IBM AI experts also discussed a new foundational method known as neurosymbolic AI, which is the ability to combine statistical, data-driven learning of neural networks with the powerful knowledge representation and reasoning of symbolic approaches.

Moderator Schubmehl commented that having a combination of neurosymbolic AI and deep learning "might really be the holy grail" for advancing real-world AI.

Kaelbling agreed, adding that it may be not just those two techniques but include others as well.

One of the themes that emerged from the webinar is that there is a very helpful confluence of all types of AI that are now being used, said Cox. The next evolution of very practical AI is going to be understanding the science of finding things and building a system we can reason with and grow and learn from, and determine what is going to happen. "That will be when AI hits its stride," he said.

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The path to real-world artificial intelligence - TechRepublic

New Report from Corinium and FICO Signals Increased Demand for Artificial Intelligence in the Age of COVID-19 – PRNewswire

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Today, FICO, a global analytics software firm, released a new report from the market intelligence firm Corinium that found the demand for artificial intelligence (AI), data, and digital tools is soaring as the COVID-19 pandemic continues to put a strain on many enterprises.

Conducted by Corinium and sponsored by FICO, the report - Building AI-Driven Enterprises in a Disrupted Environment - surveyed more than 100 c-level analytic and data executives and conducted in-depth interviews to understand how organizations are developing and deploying AI capabilities. The study found that the uncertainties caused by the pandemic have forced many organizations to adopt a more committed, disciplined approach to becoming an AI-driven enterprise, with more than half (57 percent) of the chief data and analytics officers saying that COVID-19 has increased demand for AI, digital products and tools.

Enterprises are seeking new AI-driven ways to mitigate risks and navigate through uncharted territories in the current economic environment. The report reveals the central role AI has in shaping the future as global markets work through and begin to recover from COVID-19; as well as how to mitigate future risk and disruption going forward.

Some key findings include:

Organizations Rally to Add AI CapacityMost data-driven enterprises are now aggressively investing in their AI capabilities, in fact 63 percent of respondents have started scaling AI capacity within their organization. However, enterprise chief data and chief analytics officers are facing a wide range of challenges as they increasingly look to grow AI. 93 percent say ethical considerations represent a barrier to AI adoption. Other barriers identified include:

Ethical and Responsible AIMore than 93 percent of respondents said that ethical considerations represented a barrier to AI adoption within their organizations. However, as pointed out in the report, "ensuring AI is used responsibly and ethically in business context is a huge, but critical task."

Half of survey respondents said they have strong model governance and management rules in place to support ethical AI usage, making this the most common approach to tackling the challenge. However, more work is needed to ensure ethical AI usage as 67 percent of AI leaders don't monitor their models to ensure their continued accuracy and ethical treatment.

"Being ethical is not being blind to what's in the model," said Dr. Scott Zoldi, chief analytics officer, FICO. "Organizations need to ensure that AI is designed robustly and is explainable, transparent, built ethically and governed by auditable, recorded development process that is referenced as data shifts over time."

When asked which business areas are pushing for greater AI responsibility with an organization, data and analytics leader said:

AI Enables Post-COVID Competitive AdvantageFrom better customer experiences and reducing financial crime to automating business processes and improving risk management, respondents believe AI will help their organizations secure a competitive advantage.

A complete copy of the FICO sponsored report, Building AI-Driven Enterprises in a Disrupted Environment, can be downloaded here.

About FICOFICO (NYSE:FICO) powers decisions that help people and businesses around the world prosper. Founded in 1956 and based in Silicon Valley, the company is a pioneer in the use of predictive analytics and data science to improve operational decisions. FICO holds more than 195 US and foreign patents on technologies that increase profitability, customer satisfaction and growth for businesses in financial services, telecommunications, health care, retail and many other industries. Using FICO solutions, businesses in more than 100 countries do everything from protecting 2.6 billion payment cards from fraud, to helping people get credit, to ensuring that millions of airplanes and rental cars are in the right place at the right time.Learn more athttp://www.fico.com.

Join the conversation athttps://twitter.com/fico&http://www.fico.com/en/blogs/.

For FICO news and media resources, visitwww.fico.com/news.

FICO is a registered trademark of Fair Isaac Corporation inthe United Statesand in other countries.

SOURCE FICO

https://www.fico.com

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New Report from Corinium and FICO Signals Increased Demand for Artificial Intelligence in the Age of COVID-19 - PRNewswire

(P) Huawei: Artificial intelligence will help the EU achieve its Farm to Fork sustainable farming strategy – Romania-Insider.com

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(P) Huawei: Artificial intelligence will help the EU achieve its Farm to Fork sustainable farming strategy

The Farm to Fork strategy, which the European Commission presented on May 20, is central to the European Green Deal. Its main aim is to make food production and consumption sustainable for European citizens.

"Europe needs to seize the opportunities offered by AI in the area of farming. This will involve substantial investments and a regulatory approach creating an open ecosystem," Abraham Liu said.

Connecting, collecting, and analyzing big data will be vital to maximizing efficiency, increasing productivity, and reducing CO2 emissions to meet climate targets, and AI will play an essential role in these.

The new European strategy wants to find innovative solutions to mitigate climate change while helping farmers achieve more productivity and higher yields.

"Connectivity is also very important. Without it, none of the ambitions and policies, including those linked to Sustainable Development Goals and the Farm to Fork strategy, will be possible," added the Huawei representative to the EU institutions.

"Everything flows from Connectivity. With Connectivity comes AI capability a crucial component of the Farm to Fork Strategy and with AI comes reduced costs for farmers, improved soil management, a reduction in the use of pesticides, freshwater, and greenhouse gas emissions," Abraham Liu added.

The online debate AI in Farming: making the 'Farm to Fork' agenda a global standard for sustainability?, organized by Public Affairs Bruxelles in partnership with Huawei and supported by GeSI, SMARTKAS and DroneThinkDo brought together policymakers, think tanks and representatives of the agricultural and digital sectors. They discussed the best approaches for supporting the Commission's strategy.

Academia and industry should work together to drive innovation and develop standards, while governments need to invest in training and up-skilling for people. This way, they would play a full role in the economy, and nobody would be left behind, added Abraham Liu.

For Huawei, Artificial Intelligence is at the heart of Everything. "We are investing in open, online learning, to give people the basic skills they need to get employment and bridge the digital skills gap. We are using mobile classrooms to bring digital skills to under-served and remote communities," said Huawei's Liu.

In 2018, using AI and augmented reality, Huawei created StorySign - the world's first literacy platform for deaf children, a free app that translates the text from selected books into sign language. The company is also working on an AI-based device for non-trained professionals to identify children with visual disorders as early as possible. Another app by Huawei called Facing Emotions, allows the visually impaired to "see" the emotion on someone's face by using the rear camera of Huawei's HUAWEI Mate 20 Pro phone and by analyzing their expression using artificial intelligence (AI).

The company has also Huawei has taught its HUAWEI Mate 20 Pro smartphone to compose the third and fourth movements of Schubert's famously 'Unfinished Symphony,' by using the power of Artificial Intelligence.

Huawei's recently announced Ascend family of AI chips would power a full range of AI scenarios for customers and partners. They will provide AI capabilities for public and private clouds, the industrial Internet of Things, consumer devices such as smartphones and wearables, and the edge environments that bring Everything together.

Huawei, a leading global provider of information and communications technology (ICT) infrastructure and smart devices, has more than 194,000 employees and operates in over 170 countries and regions. Founded in 1987, it is a private company fully owned by its employees.

In Europe, Huawei currently employs over 13,300 staff and runs two regional offices and 23 R&D sites. So far, Huawei has established 230 technical cooperation projects and has partnered with over 150 universities across Europe. It offers integrated solutions across four key domains: telecom, IT, smart devices, and cloud services.

Photo source:courtesy of EU Reporter.

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Murlo connects artificial intelligence with organic folklore in ‘Primal’ – FACT

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Taken from his new EP, which centres around forest dwellers that mythologize A.I. technology.

Murlo has returned to the universe he intricately wove together with the sounds and images of his debut album Dolos, expanding its mythos with a new four-track EP.

Primed for Primal focuses on a group of forest dwellers that mythologize artificial intelligence and modern technology, worshipping them in ceremonies that are reminiscent of pre-Roman rituals surrounding nature, the sun and its associated deities.

Murlo illustrated and animated the visuals for the first track to be released from the EP, Primal. In the same way that Dolos was released alongside a 36-page graphic novel, the physical release of the EP will include four prints of his hand-drawn illustrations that expand the universe of the projects further.

Primed for Primal arrives on August 14 and is available to pre-order now, on Coil Records.

Watch next: Samuel Kerridge and Taylor Burch channel Jean Cocteau for AV album, The Other

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Artificial Intelligence (AI) Verticals Market Analysis by Current Industry Status & Growth Opportunities, Top Key Players, Target Audience and…

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Jackson County Sheriff’s Office: Legally carrying a gun while wearing a mask is OK – WAAY

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In Jackson County, the sheriff's office said they received several calls about legally carrying a gun while having a mask on.

Rocky Harnen, chief deputy for Jackson County, said after the governor announced a mandatory masking order, the sheriff's office got multiple calls from people asking if they're able to legally carry a gun with a permit and wear a mask at the same time.

Harnen says the answer is yes.

"There is nothing that prohibits you from carrying a gun, concealing it with a concealed carry permit and having a mask on," said Harnen.

He says the sheriff's office wants to make this message clear.

"We certainly support the Second Amendment, right to carry a weapon, if you have a pistol permit, carry and conceal that is absolutely fine. Wear a mask, do what the governor says, and let's get rid of this thing," said Harnen.

Harnen said as for enforcing the statewide mandatory mask order, the office is still reviewing how to enforce it if necessary but encourages people to follow the governor's order.

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Jackson County Sheriff's Office: Legally carrying a gun while wearing a mask is OK - WAAY