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Valued to be $4.9 Billion by 2026, Artificial Intelligence (AI) in Oil & Gas Slated for Robust Growth Worldwide – thepress.net

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Valued to be $4.9 Billion by 2026, Artificial Intelligence (AI) in Oil & Gas Slated for Robust Growth Worldwide - thepress.net

Artificial Intelligence And Subject Matter Eligibility In US Patent Office Appeals Part One Of Three – Intellectual Property – United States – Mondaq…

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Note: First published inThe Intellectual PropertyStrategistandLaw.com.

This article is Part One of a Three-Part Article Series

Artificial intelligence is changing industry and society, andmetrics at the US Patent and Trademark Office (USPTO) reflect itsimpact. In a recent publication, the USPTO indicated that from 2002to 2018 the share of all patent applications relating to artificialintelligence grew from 9% to approximately16%.SeeInventing AI, Tracing thediffusion of artificial intelligence with U.S. patents,Office of the Chief Economist, IP Data Highlights (October 2020).For the foreseeable future, patent applications involvingartificial intelligence technologies, including machine learning,will increase with the continued proliferation of suchtechnologies. However, subject matter eligibility can be asignificant challenge in securing patents on artificialintelligence and machine learning.

This three-part article series explores USPTO handlingofAliceissues involving artificialintelligence and machine learning through a sampling of recentPatent Trial and Appeal Board (PTAB) decisions.See AliceCorp. v. CLS Bank Int'l, 134 S. Ct. 2347 (2014). Somedecisions dutifully applied USPTO guidelines on subject mattereligibility, including Example 39 thereof, to resolve appeal issuesbrought to the PTAB. In one case, the PTABsuasponteoffered eligibility guidance even withnoAliceappeal issue before it. These decisionsinform strategies to optimize patent drafting and prosecution forartificial intelligence and machine learning relatedinventions.

Generic Machine LearningAlgorithm

InEx parte Hussain, Appeal No. 2020-005406 (PTABFeb. 18, 2021), the PTAB considered the subject matter eligibilityof claims reciting a machine learning algorithm inrelation to mitigation of risk of consumer default on an onlinetransaction. Representative claim 1 recited as follows:

Id.at 2-3 (emphasis added). To assess subjectmatter eligibility of the representative claim, the PTAB appliedUSPTO guidelines mandating the familiar two step analyticalframework.SeeUSPTO, 2019 Revised PatentSubject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7,2019); USPTO, October 2019 Update: Subject Matter Eligibility, 84Fed. Reg. 55942 (Oct. 17, 2019).

As to the first prong of Step 2A in the analytical framework,the PTAB indicated that the representative claim used only ageneric machine learning algorithm to output afidelity score in some unspecified manner. The PTABalso addressed Example 39 of the USPTO guidelines, the examplereciting machine learning in a hypothetical claim deemed eligible.In particular, the PTAB contrasted relevant detail in the claim ofExample 39 versus the relative absence of such detail in therepresentative claim. The PTAB acknowledged that the representativeclaim expressly recited that the machine learning algorithm wastrained to infer characteristics about a user from variable valuesgenerated from specific types of data. Nonetheless, the PTABreiterated that the machine learning algorithm as claimed wastrained to make inferences in an unspecified way without anytechnical details. The PTAB gave little consideration to therecited transformation of the specific types of data into thevariable values, which were specifically claimed as inputs to themachine learning algorithm. Depending on the facts, the claimedinputs to the machine learning algorithm could have been deemedsuggestive of data to train the machine learning algorithm. Forthat reason, the claimed inputs might have been argued topotentially resemble or parallel the recitation of training datadetails supporting eligibility in Example 39. However, no sucharguments were raised.

The PTAB found that a machine learning algorithm assuch was not described in the specification despitethe acknowledged references in the specification to a logisticregression, random forest, supervised learning algorithm, neuralnetwork, vector machine, and other classification algorithm.According to the PTAB, the description of these otherconcepts without technical details confirmed the abstractnature of the claimed machine learning algorithm. In particular,the PTAB noted that the specification described algorithms togenerate fidelity scores without details of trainingthem to infer characteristics about users. Refusing to alsoconsider the machine learning claim limitations under the secondprong because they recited the abstract idea under the first prong,the PTAB ultimately determined that the representative claim wasineligible after finding no inventive concept.

Accordingly, not just any claimed specifics about an artificialintelligence related invention will satisfy the PTAB abouteligibility. Although the representative claiminHussainrecited the inference specificallygenerated by the machine learning algorithm, the PTAB indicatedthat the claim still did not specify enough. In view of thePTAB's observation that both the specification andrepresentative claim lacked technical detail, expressly claimingtraining data and identifying it as such and of coursebeforehand drafting the patent application in support thereof might have secured a different outcome.

Part Two of this article series will further analyze recent PTABdecision making regarding artificial intelligence and subjectmatter eligibility.

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

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COVID-19: quality of life and artificial intelligence | JMDH – Dove Medical Press

Introduction

History has a way of reminding us that while the good times are great, a business as usual comes with many unforeseen risks and challenges. On a positive note, stress, anxiety, and other mental health issues have turned around many mindsets in certain groups. There are now significant and unprecedented levels of compassion, empathy, and more, originating from many populations. One such instance, wherein significant challenges were posed to the community is at the time of the First World War. Besides, there was the Spanish plague, there was the second world war and for the last 60 plus years, we have had to live in a world of misgivings; ranging from populism to political unrests and instability in several parts of the world, primarily the Middle East and some parts of Asia.

When the current Coronavirus disease 2019 (COVID-19) started in December 2019, many assumed that like its predecessors H1N1, SARS, different plagues, and viruses, etc., it was going to pass with a thud (Chatterjee et al 2020: para 9).1 Five months into the pandemic and countries continue to live in fear, driven by many unknowns and limited scientific evidence. In the meantime, this aggressive, stealth, and brutal virus continues to spiral unabated. There is at least some consensus that once the peak of the pandemic has been achieved, there will be a reason for optimism. This is based on the assumption that everything being equal (continuous self-exclusion, personal hygiene, social distancing, etc.), the worst would then be behind us. For the most part, this assumption is correct if the processes are effectively and comprehensively implemented. The reality is that the potential for a subsequent wave is real and compelling. To be specific, as per the study findings of Salyer et al,2 the second wave of Covid-19, which was evident by December 2020, was more aggressive than the first one in several cases. In this regard, the Spanish flu, also known as the 1918 flu pandemic, serves as a classical example. Its second wave of infection proved to be even deadlier than the first after non-medical intervention measures put in place at the time had been relaxed.3

It must also be noted that during the outburst of COVID-19 also known as SARS-CoV-2 disease, healthcare workers are found to play a pivotal role. According to the report published by World Health Organization (WHO),4 healthcare workers have been providing frontline services in the pandemic. They are also found to undertake several responsibilities in maintaining health and wellbeing during the outbreak of the coronavirus such as implementing effective health measures, which, in turn, can protect the occupational health and safety aspects of the healthcare organizations. Their significant roles, as well as responsibilities in all the Covid-19 pandemic stages, are found to expose them to risks. The hazards that these healthcare workers have been immensely exposed to during this pandemic include psychological distress, pathogen exposure, fatigue, psychological violence, physical impacts, occupational burnout, extensive working hours, and stigma, among others (World Health Organization (WHO) 2021: 1).4 Even community health workers are found to be playing a vital role in facilitating successful COVID-19 vaccination programs. Health workers are found to plan, as well as coordinate the vaccine rollouts. They are also responsible for identifying the target groups for vaccination along with engaging communities, service delivery, facilitating mobilization, tracking progress, and conduct follow-ups (World Health Organization and the United Nations Childrens Fund (UNICEF) 2021: 8).5

Additionally, Al Thobaitya and Alshammari6 asserted that healthcare workers and nurses have played a significant role in disasters and daily routine, especially during the COVID-19 pandemic. They are engaged in providing holistic care to all patients. Since nurses constitute many of the healthcare professionals, they have an important role within healthcare systems. Specifically,

Their roles in treating patients with COVID-19 involve triaging patients and detecting suspected cases with infections; providing essential treatment in an emergency and dealing with suspected patients with precautions; helping in decontamination and coordination with other healthcare providers; supplying holistic nursing practices in managing multiple infections simultaneously; playing critical roles in expanding care services; and dealing with relatives.6

However, Lahner et al7 stated that due to their pivotal role in maintaining the health and wellbeing of the patients even during Covid-19, health workers are found to be at a high risk of getting infected. In the context of Covid-19, it has highly influenced the dynamics of quality of life along with incorporating AI. This has been particularly highlighted in the scientific research conducted by Laudanski et al.8 In this study, it has been understood that technological advancements of AI have significant scope to improve the pandemic response at every stage. Appendix 1 illustrates the pandemic phases propounded by the WHO, wherein distinctive AI applications have been visible considering hypothetical cases. It shows that in the majority of all the stages, AI can be applied in one way or the other. It is during this pandemic that AI engines have been prominently performing with a higher level of sensitivity. This has helped to track cases along with the performance of response programs. Even in cases, wherei limited data are available, AI can be developed and deployed. However, pre-training of AI is found to be highly necessary so that appropriate outcomes can be attained.9

Hence, with the consideration of the COVID-19 Pandemic, the transformation, which has been evident across the world concerning the quality of life as well as AI technological advancements, will be explored in this research paper. The key objective of this research paper is to perform an exploratory review of the varied dynamics of the COVID-19 pandemic, in addition to emphasizing the theme of pandemic morbidity and mortality. AI and its contributing role will also be reviewed. The reason for conducting this exploratory review on the concerned topic is to explore the pandemic dynamics, and its contribution in addressing such issues in the future. The present study indicates that it has a high contribution to the existing literature. This is because this topic can be relevant to other health and social issues. For understanding the literature gap, a literature review has been conducted. Thus, it must be noted that limited literature is present, which examines the dynamics of AI and QOL concerning the recent outbreak of the COVID-19 pandemic. Therefore, it can be evident that this study can provide important information concerning the QOL and AI dynamics during the pandemic.

The method, which has been incorporated in this research study, is a review of the literature. Besides, anecdotal evidence along with exploratory reviews and reports on the morbidity of COVID-19 have also been taken into due consideration for understanding the dynamics of QOL and AI. This research paper also provides the scope of the devastating effects of the pandemic in select countries: a challenge that should awake all policymakers and create scope for more innovative, cost-effective, and pragmatic interventions. In that regard, the importance of supply chain management systems cannot be adequately emphasized. For the study, a literature review has been conducted by collecting reports and anecdotal evidence. Only recent sources have been selected or included for exploring the review. This is because the issue of the pandemic is recent. Hence, only recent sources are valid for the study. The sources before 2019 have been excluded from the study.

A troublesome pre-occupation in many affected regions is vulnerability. The notion that we are all equal in the fight against this virus has been quickly dispelled with early findings, revealing health inequalities amongst populations ranging from front-line service providers to marginalized communities to racial minority groups (Centers for Disease Control and Prevention (CDC) 2020).10

Specifically, in the United States (US), preliminary nationwide data released by the Centers for Disease Control and Prevention (CDC)11 revealed that although African Americans represent approximately 13% of the US population, they accounted for 30% of all COVID-19 patients. Although far from complete, these data are consistent with the findings from other data collected on race and COVID-19 so far. A disproportionate toll is also being seen in the UK after the Guardian did an analysis of 12, 593 patients who died of COVID-19 as of April 19, 2020. It showed that 19% were Black, Asian, and minority ethnic (BAME) even though they make up 15% of the population.12

In many cases, keeping food on the table means foregoing safe working conditions and a greater risk of exposure to COVID-19. Hence, it can be stated that this issue closely aligns with pandemic morbidity: the focus of the present paper. Besides, a lack of economic resources often translates to food insecurity, amongst other things, which in turn often leads to poorer health outcomes that include a higher risk of underlying health conditions. In India, millions of people, including migrant laborers and daily wage earners, are facing hunger since the countrys shut down in late March 2020 left them with no means to earn a living. A similar dire outlook is also threatening First Nations communities in Canada and black communities in the US. Canada does not report Coronavirus morbidity by race or ethnicity; making it difficult to address disparities. The study conducted by Nguyen further suggested that to eliminate such economic issues, AI technology can be implemented. In this context, it has been recommended that economic recovery can be predicted, as well as tracked with the help of AI applications by detecting cars and solar panel installations in parking lots.13

Many front-line workers like transport employees, sanitary workers, delivery personnel, etc., are often made up of BAME groups.14 In New York City, for example, Blacks and Latinos make up more than 60% of the hard-hit Metropolitan Transportation Authority (MTA). As of April 22, 2020, eighty-three MTA workers have died.15 Apart from them, healthcare workers are also found to be adversely affected due to COVID-19 economically. According to Shukla, Pradhan, and Malik,16 the outbreak of COVID-19 has posed an economic impact on the healthcare sector of India. As a result of which

A stimulus package at 0.8% of GDP was announced on 26 March 2020 and included in-kind and cash transfer to lower income households, insurance coverage of healthcare workers and financial support to low wage workers and others seeking jobs.16

Even the most basic health recommendations to avoid contracting or spreading infection like hand washing and social distancing are major challenges in marginalized communities without sufficient access to water or housing. The number of people who do not have regular access to water is mind-boggling: 36 million people in Mexico, over 2 million in the US, more than 100 in First Nations communities in Canada, 63.4 million in India, etc. In all, 40% of the worlds population lack access to basic hand-washing facilities in their homes.1720

The inability to self-isolate, when faced with a virulent virus, places additional stress on people within communities, who are affected by overcrowding and housing shortages. In many Indigenous communities in Canada often living in remote areas with limited medical services there are sometimes two or three families living in the same house.21 Indigenous Australians face the same troubling dilemma, compounded by a higher prevalence of underlying health conditions in Indigenous communities compared to general populations.21 There is compelling evidence that unprecedented measures such as national lockdowns were incorporated in Italy due to the pandemic. The main reason for undertaking such measures was that Italian people were facing several health issues, including psychological issues. Even post-traumatic symptoms were evident and hence, psychological interventions were suggested in the study present by Roma et al.22

For Brazils Indigenous groups, where some have little or no contact with non-Indigenous society leaves them particularly vulnerable to disease. Fears grow that the entire community could be wiped out amidst a rising number of illegal land invasions from loggers, miners, etc. As of April 17, 2020, Brazils Socio-Environmental Institute (ISA) has recorded at least 27 confirmed COVID-19 cases and 3 deaths, including a 15-year-old from a village on the Uraricoera River - an access route for gold rush miners.23 Besides, in South-East Asia, it has been reported that Covid-19 was evident earlier than in other parts of the world. The concerned states took 17 days to declare an emergency ie, after 50 positive cases of the contamination of the virus.24 Similarly, several African nations have recorded lesser than 1000 cases. Specifically,

WHO has warned that the pandemic could kill between 83,000 and 190,000 people in 47 African countries in the first year, mostly depending on governments responses; and the virus could smolder for several years.25

Based on the understanding derived from the preliminary research, it has been found that due to the significant roles and responsibilities undertaken by the healthcare workers, they become prone to being infected by the virus. This is the reason why Lahner et al. affirmed that there is a high prevalence of COVID-19 infection among healthcare workers. This was prominently evident from the cross-sectional study, which was done considering the retrospective data of healthcare workers. The results of this study showed that

A total of 2057 HWs (median age 46, 1969 years, females 60.2%) were assessed by the RNA RT-PCR assay and 58 (2.7%) tested positive for SARS-CoV-2 infection. Compared with negative HWs, SARS-CoV-2-positives were younger (mean age 41.7 versus 45.2, p < 0.01; 50% versus 31% under or equal to 40 years old, p < 0.002) and had a shorter duration of employment (64 versus 125 months, p = 0.02). Exposure to SARS-CoV-2 was more frequent in positive HWs than in negatives (55.2% versus 27.5%, p < 0.0001).7

It was further observed that nearly half of the healthcare workers considered for this study were not exposed to any COVID-19 infected subjects. This helps in assesing the vulnerability of the healthcare workers while dealing and responding to the pandemic because they are playing the essential role of the frontline workers.7 This study is found to significantly contribute to the literature review. The main reason being that in conducting vaccination drives, the healthcare professionals have important roles. However, if they are affected, the healthcare programs may not lead to positive outcomes. This study can be used in the future for exploring the situations and understanding the risks that are associated with frontline workers so that the third wave of COVID-19 can be managed appropriately along with responding to future healthcare issues.

While lockdowns continue to serve as a geopolitical prevention strategy against COVID-19, the financial and economic outcomes on the poor populations undoubtedly are remarkably onerous. In Asia, for example, and according to the Economic and Social Commission for Asia and the Pacific (ESCAP), 70% of workers belong to the informal economy (no benefits or safety net).26 Many countries in this region have introduced support mechanisms financial and economic (rice, sugar, etc.). These strategies are necessary but not sufficient! As demonstrated by the lockdown insubordination in countries like Bangladesh, the poor in these economies remain vulnerable with limited options and an extremely unenviable way of life: contract the virus by risking going out or follow the lockdown and starve.

The biggest concern for the World Health Organization (WHO) is COVID-19s potential to spread in countries with weak health systems. While the 2019 Global Health Security Index, a health security assessment listing of 195 countries, highlighted fundamental weaknesses of healthcare systems around the world, its not surprising that many countries found to be the least prepared were in Africa.27 Less than 50% of the continents population has access to modern health facilities and countries are plagued with shortages ranging from low numbers of healthcare workers in ratio to the population to medical equipment, medications, and capacity (AFRIC 2019).28

Densely populated cities, slums, and displacement camps; struggles with other simultaneous communicable diseases, ongoing conflicts in some regions, and myriads of other dangerous conditions, make it inevitable that the continent will experience a substantial epidemic.

The one silver lining in terms of mortality rates is that Africa has the youngest population in the world 60% of its 1.25 billion population is under the age of twenty-five, an age group likely to recover from COVID-19 infection.

Besides, data collected from the Chinese Center for Disease Control and Prevention (China CDC) in January and February 2020, identified people aged 60 and over as the most vulnerable to COVID-19. Mortality rates based on these findings were determined by University of Bern researchers as 4.6% for ages 6069, 9.8% for ages 7079, and 18% for ages 80 and over.

Unsurprisingly, with 23.1% of Italys population being 65 and over, it has one of the highest mortality rates in the world (28,236 as of May 1, 2020). In Canada, 79% of all deaths in the country have been linked to seniors homes and long-term care facilities as of April 13, 2020, according to chief public health officer Theresa Tam. Similarly, as per the study conducted by Bhapkar et al, the mortality rate during the pandemic is constantly altering with time and hence, it has been termed Progressive Mortality Rate (PMR). In this study, it was observed that the PMR rates of Russia, India, Japan, the US, Brazil, Germany, China, Mexico, Singapore, New Zealand, and Canada were 1.83, 2.82, 2.75, 3.61, 3.92, 4.35, 5.34, 12.79, 0.05, 1.4, and 7.63 respectively. On the other hand, Progressive Recovery Rates (PRR) of the same countries were recorded to be 85.58, 106.44, 101.15, 38.89, 96.53, 94.85, 94.44, 95.6, 97.93, 97.7, and 92.63 accordingly.29

Furthermore, the study of Samlani et al, suggested that in Morocco, the quality of life of the people was moderately affected by the pandemic. This was because the Mental Health Score (MCS) of all the participants was 34.49. On the other hand, their Physical Health Score (PCS) accounted for 36.10. It was also found that the impact of the concerned pandemic was evident in those people with chronic illnesses, which significantly deteriorated their wellbeing and quality of life. The main reason for such results is that people with or without chronic illness were found to suffer from mental health and panic issues. Besides, the isolation and quarantine made people face psychological health problems.30 It has also been observed that Covid-19 has led to the death of several people, which has further affected the food systems and presented unprecedented challenges to work-life and public health.31

On the other hand, as of March 2021, a total of 1,521,068 people have been infected by the pandemic in South Africa and the most affected region was Gauteng (Johannesburg), which reported about 406,729 Covid-19 cases. It was also found that the highest increase in the daily cases of Coronavirus was evident on 8th January 2021 with 21, 980 new cases. Besides, viewing from a different perspective, it has been found that the pandemic significantly hampered the businesses across the nation, thereby adversely affecting their survivability at large.32,33 This indicates that South Africa has been largely affected by the pandemic, which is bound to change the quality of life of people living and working therein. Concerning South Africa, Covid-19 largely influenced the deaths and mortality rates of the nation due to the presence of underlying causes. It has made a significant impact on the quality of life. Contextually, the mental health of people was negatively affected by the pandemic due to the uncertainty that it created. Besides, restrictions, quarantine, financial losses, high infectivity, continuous lockdowns, fatality, and unemployment rates have altered the daily lives, as well as activities of people. This has led problems associated with mental health along with substance abuse. Even educational institutions have remained closed, which negatively affected the learning and teaching activities of people. Even teenage marriages were observed to increase along with gender-based violence, demonstrations, and social unrest. This implies that there are less human capital and economic opportunities in the future of the nation.34

Another study conducted by Guo et al portrayed those lockdowns, which have been implemented as a precautionary measure during the Covid-19 pandemic have significantly influenced the quality of life of people with Parkinson's disease (PD). In this regard, it was found that the concerned patients were unable to seek medical advice or guidance from their respective doctors. As a result, most of the patients had to alter their routine medicines, which made their quality of life or health conditions even worse. In such situations, telemedicine is found to be significantly effective and efficient for the patients during the lockdown. The challenges concerning adequate treatment caused the symptoms of patients to get aggravated, which further exacerbated their quality of life. On the other hand, healthcare professionals are also finding it difficult to maintain healthcare quality.35,36 Zhang and Ma further affirmed from their study that the quality of life, as well as mental health of local people, especially that of China has deteriorated significantly. Specifically, a mild level of stress was evident among most of the survey participants irrespective of the devastating pandemic outbreak. The mean Impact of Event Scale (IES) score was found to be 13.6 7.7.37 Even social and economic developments have been adversely affected, thereby increasing poverty along with inequality.38 All these aspects indicate that Covid-19 has largely affected people throughout the world, thereby transforming the way they live or their quality of life.

On a similar note, Dey et al highlighted that because of COVID-19, there are several psychosocial and psychological impacts: especially fear among the public. In this review, it was particularly found that the psychological effect was more taxing. Hence, long-term quarantine was implemented by the governmental bodies of various countries. This is the reason why boredom, fear, and frustration have been observed to be highly evident among the citizens. This has increased the difficulties in the trying times of the Covid-19 outbreak. The latter stages of the pandemic were observed to pose more significant impacts such as psychological disorder and stress along with mental stigma and financial losses. This study found that 22% of adults (a survey among 1000 people) have been experiencing worse sleep patterns during this pandemic, which may increase the risk of cardiovascular events [9]. In this situation of adversity, yoga, meditation, and video chat with relatives and friends induce mental relaxation, to some extent. In contrast, self-isolation gives us opportunities to connect with our passions and inner identity.39

Additionally, AI along with augmented intelligence plays a significant role in understanding the collected data through data analytics, pattern recognition, anomaly detection, and machine learning.40 Similarly, Mukherjee et al stated that AI-driven tools have been used to track as well as observe the developments of positive cases during the outbreak of the pandemic. However, it was argued that differences in data can influence the critical decision-making concerning the preparedness and responses of the pandemic. With the advancement in the pandemic stages, technical innovations concerning AI have also been evident, especially for detecting and predicting purposes.41

Currently, there are several achievements, which have been evident during the outbreak of a pandemic. According to report findings of United Nations, telephone-enabled services such as teleconferencing along with social media and other smartphone applications as well as online shopping have been increasing. These services are used to resolve the problems due to Covid-19 in most of the nations, including the US and China. These improvements have increased e-commerce business activities and forced traditional businesses to undergo digitalization.42 In the social context, one of the positive aspects, which have been highlighted by the pandemic, is the role and contribution of women in society. Cities and communities have facilitated innovation for achieving sustainable developments even in this crisis. Besides, marine, as well as land ecosystems are also improving during this pandemic due to reduced exploitation of resources. Also, due to lockdowns and isolations, the flora and fauna are being restored in their natural habitat, as they are not disturbed by humans. Another positive aspect of this pandemic is the unity with which people have been fighting against Coronavirus.43,44 Furthermore, Covid-19 has facilitated the importance of distance learning. However, there are students, who are facing problems in switching to the online mode of learning due to the lack of adequate resources and support from their parents IESALC 2020: 45; UNESCO 2020).4547 On the contrary, Gonzalez et al48 affirmed that the confinement evident due to Covid-19 had a positive impact on the performance of the students in Spain. Similarly, it was found in the study conducted by Chaudhary, Gupta, Jain, and Santosh that the air quality was considerably improved during the lockdown phase of the COVID-19 pandemic in most nations. Hence, it can be stated that due to COVID-19, isolation practices were implemented, which proved to be climate favorable. In many regions of the US, Brazil, China, and India, air quality indices improved due to restrictions in air pollution activities.49 Besides, currently, big data and AI incorporation have been evidenced to enhance the pandemic situation and reduce the adverse impacts of COVID-19. In this context, it was found that By training on an open-source dataset with 13, 975 images of 13, 870 patients, the proposed CNN model can achieve an accuracy of 93.3%. (p. 5).50 Herein, CNN model refers to the convolution neural network (CNN), which incorporates AI techniques.50

Ethical issues are being faced in several areas during the pandemic, especially in terms of physical distancing, conducting clinical trials, rights of healthcare workers, priority-setting, public health surveillance, and resource allocation. The ethical issues are mainly at the time of conducting healthcare research, policy-making, and decision-making process.51 Hence, ethical aspects must be closely considered while responding to the issue at the post-pandemic stage. Specifically, ethical concerns have emerged with the increase in the influx of patients requiring ICUs. Healthcare professionals have been facing ethical dilemmas along with life-support withdrawal decisions. Similar issues have also been faced concerning the quality of end-of-life support and family visits. Hence, effective triage policies are to be formulated so that these issues may not be faced in the post-pandemic phase.52 Similar aspects have been highlighted by McGuire et al wherein it has been affirmed that ethical issues emerged not only within the healthcare system but also in society. Particularly, ethical issues can be evident while defining the benefits, handling informed consent, understanding the special needs of other patients, mitigating discrimination, identifying structural inequalities, and engaging communities.53 Ethical issues have also been found to emerge at the time of resetting healthcare services after the outbreak ie, post-pandemic.8 Contextually, it has been affirmed in the study of Laudanski et al that

Numerous predictive models of COVID-19 prognosis in various individuals based on AI-driven algorithms have been designed and published [7580]. Their ability to distinguish between favorable outcomes and demise is significantly accurate. A few of them were implemented to test their suggestions in real life, a fact that leaves unaddressed concerns about dataset impartiality and concomitant ethical concerns about the implication of AIdriven decisions.

This indicates that in the post-pandemic era, ethical concerns have been prominent, especially at the time of implementing AI-driven decisions.54

The latest technology has been of utmost importance during the pandemic. This is because AI is found to be effective not only in detecting pathogens but also in responding and recovering from Covid-19. According to a report presented by OECD, AI systems had predicted an outbreak of pneumonia in China before coronavirus became the worldwide threat. Hence, understanding the effectiveness of this technology, it is clear that AI technologies and tools can be incorporated for supporting the efforts of medical communities, policymakers, and societies. This can enable the concerned authorities to manage activities at all stages of the pandemic, including the acceleration of research, detection, response, prevention, and recovery. AI can be effective in enhancing research for the discovery of proper solutions such as vaccines and drugs through distributed computing and open data projects.55 Similar opinions have been provided by Arora, Banerjee, and Narasu, wherein AI largely contributed to developing several types of vaccines to date. It seemed that there is a race between the virus and vaccine developers. Hence, for the betterment of mankind and to improve the situation created by the pandemic, AIs ability continues to be vital. This was because The pace of the discovery can be accelerated manifold by harnessing the power of AI.56

To win in the race, several biotechnology companies are depending on AI such as Blue Dot for pacing up the ways to find a cure for the virus. This technology has the potential to identify changes and spot patterns so that the process of vaccine development does not get hampered. In this context, several successful trials have been made. For instance, the Deep Learning-Based Drug Screening method was created using DenseNet for predicting the interactions between ligands and proteins, which further helped in determining the drug combination that worked well while responding to Covid-19. Besides, DeepMind has used the AlphaFold library for understanding the protein structure of the virus. Furthermore, Machine learning (ML) models were developed by the AI scientists of Wuhan for identifying the infection intensity with the help of factors such as gender and age.57 As a result of such initiatives, an AI-based flu vaccine has been developed in the US for which the clinical trials are being sponsored by the National Institute of Allergy and Infectious Diseases. The scientists of Flinders University used synthetic chemist, which is an AI program that generated numerous synthetic compounds. They also used the Search Algorithm for Ligands (SAM), which is an AI program that assisted the scientists to determine good candidates for vaccine trials. This program has shortened the development process of vaccines. This indicates that AI can contribute not only to examine the drugs that are currently available but also helps in accelerating the antivirus development procedure.58 Additionally, the Human Vaccines Project, as well as the Harvard T.H. Chan School of Public Health, has started the Human Immunomics Initiative.This initiative made use of AI models to speed up the process of vaccine and therapeutic development, thereby understanding effective immunity concerning old-aged populations.59

The pandemic has also illustrated that with cooperation at the local, national, and global levels communities can thrive in the wake of the crisis.60 It has also been understood that at the time of pandemic without effective control and prevention measures, the healthcare systems become restricted when considering general measures such as limited travel, social contact, hygiene and sanitary measures, usage of PPE, isolation, and quarantine.61,62 The ongoing carnage experienced by this population is not only despicable but also confirms the degree of incompetence and lackadaisical efforts of some institutions both government and private.

As counterfactuals, there are compelling needs to know if these gruesome and unacceptable mortality rates could have been avoided if:

The memories of this pandemic in these vulnerable communities will be long-lasting and tenuous, especially between the affected families and these institutions.

Additionally, it must also be noted that communities need to prioritize and appreciate essential values along with their needs so that the true importance of healthcare professionals and frontline workers in maintaining the wellbeing of people can be understood. Even the businesses require focusing on values and fulfill the needs of the people. Piccialli et al affirmed that AI technologies have the potential to be successfully used in healthcare systems so that society can be benefitted in future pandemic situations.63

Irrespective of several positive achievements evident during the pandemic due to lockdowns and less human intentions on social, business, and environmental aspects, it has posed significant adverse impacts. To minimize or mitigate the negative consequences of the Covid-19 pandemic, certain strategic decisions need to be undertaken by the nations at the post-pandemic stage. Innovation has been one of the widely used strategic initiatives to be undertaken by several countries, especially to revive the healthcare systems along with gain economic stability.64 On the other hand, a recent Organisation for Economic Co-operation and Development (OECD) report highlighted that the social economy has been playing an essential role in addressing or minimizing the impacts of the pandemic. This indicates that nations must focus on strengthening their social economies so that both long-term and short-term impacts can be eliminated during the post-pandemic phase. This is because social economy firms have the potential to reshape the national economy, thereby encouraging sustainable economic along with inclusive models. This, in turn, can facilitate social innovation, which will help the economy to improve in the future.65 It has also been suggested by Piccialli et al that in the post-pandemic era, careful application of AI technologies must be enabled for managing complex situations similar to COVID-19 in the future, thereby involving research, healthcare, and society.63

As we go through these trying times, there is a need to regularly remind ourselves that while the vulnerable groups on the front lines specifically continue to subject themselves to this devastating virus, their motivation and dedication to respond to this professional call of duty requires special recognition, empathy, and compassion at all levels. This applies specifically to health professionals who continue to expose themselves daily to alleviate the suffering of victims of the pandemic. Institutional support remains relatively inadequate and yet its involvement is a sine qua non that cannot be adequately emphasized. Institutional support needs to be strengthened, especially concerning individual risks and supply chain coordination.

In the future, it will be important to take effective public interventions so that new cases of Covid-19 can be prevented along with mitigating community transmission. Besides, innovation must be taken into consideration for tracing cases along with online learning and telemedicine for managing the second wave of the Covid-19 outbreak more effectively than the earlier one.66 Since the second wave has been phased out; recommendations for the third wave must be taken into consideration. Vigilant monitoring of the cases must be maintained for tracking the new variants to control the cases at the earliest.67 Disparities evident during the pandemic must also be eliminated cooperatively in order to ensure that future pandemics and similar issues can be averted effectively. Additionally, the health issues such as anxiety and stress must be evaluated, as well as addressed immediately among the healthcare staff.68 It has also been understood that elderly people have higher risks of transmission, which suggests that in the future, the healthcare requirements of the older citizens must be taken into high consideration so that their safety and wellbeing can be ensured.62 Besides, the importance of AI technology has also been found to be immensely imperative, as it has been estimated to play a vital role in tackling COVID-19. AI can contribute not only to pacing up the vaccine development procedure but also in identifying future threats posed by viruses beforehand. It also helps in diagnosing, predicting infections, surveillance, gathering information, delivering materials, deploying services, and tracking the recovery process, thereby expanding strategies. Evidence of these implications has not been evident to date. This can be highly effective in tackling Covid-19 in the future.65 Additionally, AL-Hashimi and Hamdan asserted that AI has been showing positive results in detecting conditions such as diseases. It must also be effectively used in the healthcare sector. With its implentation, healthcare organizations can track the progress of any situation at a quick pace. With more advancement in AI-driven technologies, higher-quality healthcare services can be delivered for the betterment of society.69

Based on the findings gathered in the above sections, it has been understood that Covid-19 has significantly affected the world both positively and negatively. It can be concluded that the pandemic has facilitated global transformations, especially by deteriorating the quality of life of millions of people. Additionally, the public along with healthcare workers was also found to be adversely affected due to COVID-19. It became highly important on the part of the healthcare workers that their health and safety were maintained in order to perform their duties effectively. On a positive note, COVID-19 has made the best use of AI-driven technologies for aiding or responding to the pandemic. Hence, it has been suggested that its full potential needs to be explored in the future. This can help in providing better quality healthcare services in pandemic situations in the future both efficiently and effectively. This will confirm that the objective of the research study has been met effectively. Finally, an exploratory review of COVID-19 has been conducted by emphasizing the theme of pandemic morbidity and considering the dynamics of AI and Quality of Life (QOL).

The pandemic also made us realize the importance of cooperation among people along with values. It is also understood that healthcare workers and other frontline workers are vital in responding to the pandemic. Additionally, innovative approaches and effective health interventions are found to be essential in addressing the adverse consequences of the crisis. This further indicates the lessons that must be learned from the pandemic so that new waves and future epidemics can be handled as effectively as possible. One of such future implications is to ensure the health and wellbeing of elderly people. Another important future implication is to optimally utilize AI capabilities to tackle the pandemic throughout its different stages.

Personal Funds.

The author reports no conflicts of interest for this work.

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Originally posted here:
COVID-19: quality of life and artificial intelligence | JMDH - Dove Medical Press

Artificial intelligence becomes the critical enabler of future operations (Studio) – Shephard News

The US and its allies have found themselves in the middle of an AI arms race, with the prize of decision dominance on the battlefield for whoever gets there first.

Brought to you in partnership with Systel

Artificial intelligence (AI) is widely recognised as a vital military capability that will only grow in importance in the era of multi-domain operations (MDO).

But what does this mean in practical terms, and how will the technology change the modern battlefield?

In the MDO concept also known as Joint All-Domain Command and Control (JADC2) platforms and systems across land, sea, air, space and cyber will interact and reinforce one another.

To make this possible, militaries will process and exploit vast reams of data, meaning that information processing and human-machine teaming will be essential. AI can provide vital advantages in all these areas, sifting through data at a rate far beyond any human operator.

When asked what brings the urgency to this space today, defence sources stress that there is little choice. Commercial technological innovations in AI have led to rapid, transformative changes across all service branches for all major powers.

Aneesh Kothari, vice president of marketing at Systel, a manufacturer of rugged computers, highlights that the US Department of Defenses Third Offset Strategy, for instance, holds that rapid advances in AI along with robotics, autonomy, big data and increased collaboration with industry will define the next generation of warfare.

We are in the middle of an AI arms race, and the end goal is decision dominance on the battlefield, Kothari said, noting that the same impulses are driving US allies and their adversaries.

AI enables operators to move past the limits of human capacity for mission-critical data-processing workloads. It reduces a significant degree of risk to personnel on the battlefield, such as the increasing ability to deploy uncrewed vehicles.

There is a wide range of programmes aiming to exploit such advances. One example is the UK Royal Air Forces Nexus Combat Cloud, which allows data from any sensor on any platform in a given operating space to be processed in real-time. The service has also advanced a swarming drone capability through the Alvina programme.

The area has also naturally become a growing focus for industry. BAE Systems, for instance, has worked on AI in a range of areas, with some of this coming through Defense Advanced Research Project Agency (DARPA) programmes.

Such work includes MindfuL, software that can independently audit Machine Learning-based systems, helping build trust in the technology, which will be crucial as militaries boost their focus on human-machine teaming.

BAE Systems is also developing the Multi-domain Adaptive Request Service (MARS) for DARPA, which will enable semi-autonomous multi-domain mission planning.

Michael Miller, technical area director for BAE Systems FAST Labs, said that MARS significantly increases available resources, enabling battle managers to solve unforeseen requirements in a dynamic tactical environment rapidly. Crucially, the system empowers human operators, an essential element of AIs practical utility on the battlefield.

The beauty of it is that it actually allows the human to make that final decision; it helps them find important capabilities and lets them decide which is the one they prefer, Miller explained.

AI and machine learning will help not just with data processing but also managing that data.

Fundamental to MDO or JADC2 is that in great power competition, communications will not be as assured as they once were in fact, they will be under attack.

Data must be moved judiciously, while forward forces will be dispersed, disaggregated and sometimes disconnected, said Jim Wright, technical director for intelligence, surveillance and reconnaissance systems at Raytheon Intelligence & Space.

Against this backdrop, Raytheon is working on architectures in which cognitive agents manage the data flow, he said, considering the commanders intent, how the battlefield is evolving, and the threats to communications, then using this information to determine how data should be placed.

Wright noted that AI/ML would support not just data processing, but works itself into the management of data around the network.

Nevertheless, the US and its allies dont operate in isolation. As they develop their capabilities, so do potential rivals, most obviously China.

John Parachini, a senior international defence researcher at the RAND Corporation, pointed to several ways the country is applying the technology, including domestic security.

China is also making significant progress in applying AI to uncrewed vehicles, he noted. Likewise, Russia has made significant advances, particularly in the ground domain. However, the robotics must fit in with what a military force is trying to do and the environment in which it operates.

Other countries have also made substantial progress, including Israel and Turkey. Its when the systems are used in the field that you see the successes and failures which is the real way that leapfrog advances are made, Parachini said, pointing to the use of Turkish drones in Syria and other regions.

Its those experiences that will provide the lessons learned that will allow them to improve their capabilities, he argued.

AI and humans have complementary strengths and weaknesses. While AI provides unrivalled data processing and management capabilities, humans can introduce a different perspective and intuition.

When these are combined, the result is an increasingly resilient capability. For example, Miller points to tools like Google Maps, which develop a new solution if a human deviates from a route.

Similarly, in defence applications, human intuition still matters, he said: human understanding of intangibles, things that the algorithm itself cant contemplate.

Machines and humans have complementary strengths and weaknesses, Kothari noted. We must align these in the most productive way. While machines have exponentially faster abilities to crunch data, human intuition will remain critical for tactical decision making.

The human ability to see all the shades of grey, complemented by the machines ability to see black and white incredibly quickly and accurately, is a very powerful combination and a winning combination for the nation that gets it right.

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Artificial intelligence becomes the critical enabler of future operations (Studio) - Shephard News

Building Support and Addressing Concerns to Promote Artificial Intelligence in the Workplace – EnterpriseTalk

Organizations that profit the most from Artificial Intelligence (AI) projects are more likely to believe in cognitive capabilities. Winning over the end-users of AI-enabled capabilities is just as crucial, if not more, than winning over the AI-enabled capabilities themselves.

CIOs and other IT leaders who want to scale their AI programs need to win support across the organization.

According to the 2020 Market Research Report from Fortune Business Insights, artificial intelligence had a market value of US$27.23 billion in 2019 and by 2027 this figure is expected to increase nearly tenfold in just eight years, with a Compound Annual Growth Rate (CAGR) of 33.2 percent.

Here are a few steps IT leaders can take to gain buy-in and sponsorship from C-suite executives and line-of-business colleagues:

Senior executives should contribute significantly to the success of the project. Executives should collaborate with their AI teams to verify that the AI systems input and output are consistent with the companys overall digital transformation strategy. Collaborative strategy sessions with executives and AI researchers can help raise awareness of Artificial Intelligence initiatives and keep AI research and development efforts focused on business objectives.

Also Read: 4 Strategies to Enable Asynchronous Collaboration in Hybrid Work

Its critical to connect and organize advisory councils with the organizations partners, suppliers, and employees when making changes like this to gather their ideas and perspectives on implementation. Businesses will be able to know where it is wanted and needed if they do so.

Businesses should understand that while IT can help with AI-enabled innovation, it has to be a collaborative effort. Organizations should remember to foster applied interest before deciding on their next course of action.

IT leaders and managers need to illustrate how AI will help employees in order to boost user adoption within a business. Present high-quality data that demonstrates how to enhance business operations. Incentivize usage by presenting a successful use case with senior leadership support and plainly recognizable statistics. IT executives and managers should properly communicate to team members why Artificial Intelligence is helpful, and the positive impact it will have on productivity and efficiency on a daily and long-term basis.

Maintaining a people-centered mindset will go a long way. Retaining staff that have the required skills and expertise working with AI systems can pay off.

Many functional leaders are afraid of losing their jobs or becoming outdated, which is one of the barriers to AI adoption. Line managers who dont completely understand the potential of AI and ML will be overwhelmed and become defensive about the human aspects of their employment if its positioned simply as a technical upgrade or cost-saving breakthrough.

Through spotlighting that AI enables teams to focus on the actions to take based on the insights generated by AI and ML solutions rather than spending time mining the data for patterns, it becomes apparent that AI does not eliminate the need for human decision-making, but rather facilitates a more effective and efficient path to accurate results.

Also Read: Three strategies for Maximizing SaaS Spend

Business leaders could be reluctant to accept Artificial Intelligence or machine learning models outputs seriously. Changing the corporate attitude requires instilling trust by engaging with business leaders to demystify AI solutions, how they function, and how the outcomes are generated.

When business leaders recognize the value of the outcomes provided by AI solutions, they are more likely to unearth disruptive insights and be willing to use them to drive desired business goals and have a major influence across the organization.

Check Out The NewEnterprisetalk Podcast.For more such updates follow us on Google News Enterprisetalk News.

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Building Support and Addressing Concerns to Promote Artificial Intelligence in the Workplace - EnterpriseTalk

Artificial Intelligence as the core of logistics operation – mySanAntonio.com

For more technology and data that one integrates into a software, in the end always experience and learning are the fundamental pillars. The important thing is to understand how to extract them intelligently . With that phrase, lvaro Echeverra, co-founder and CEO of SimpliRoute, recalls the need that shaped the idea of creating an AI virtual assistant to optimize its logistics platform.

The startup is dedicated to optimizing routes for dispatch vehicles. The problem, according to Echeverra, was that despite the fact that logarithms and data science effectively optimize logistics a lot, there are things that no default software can evaluate, such as whether a street is in poor condition, whether it is too narrow for a truck. or if it is unsafe at a certain time. This valuable information is held by the drivers .

This premise led us to think of intelligence as the core of the operation, capable of learning from the behavior of the drivers who use the platform. Today, after more than a year of development, this has resulted in ADA, the first AI Virtual Assistant developed 100% in-house and integrated into a logistics platform, such as the popular Siri on Apple devices.

Photo: SimpleRoute

ADA has been fully integrated into SimpliRoute for a few months, and its mission is to send alerts and suggestions to drivers of companies that use the platform, in addition to collecting learning to reschedule future actions and thus further optimize routes. For example, based on learning, the AI recommends which driver should use which vehicle based on the performance of each one on historic routes; whether the company should change its fleet size based on historical utilization; o suggest optimized time windows when dispatching; among other tasks.

For us it is a big step to implement our own AI that works as a nuclear intelligence that collects the real experience in the street. Our focus as a Chilean scaleup is to be at the technological forefront in the world, and we will only achieve this by constantly improving our integration with artificial intelligence and machine learning , says the CEO of Simpliroute. .

Currently, the AI is already working together with the drivers on the new version of the app. And while for now it issues alerts and works in the background, it is expected that users will soon be able to interact directly with the AI to request information or advice.

This article originally appeared on entrepreneur.com

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Artificial Intelligence as the core of logistics operation - mySanAntonio.com

Artificial Intelligence Is Changing The Face Of Tech In …

The Tokyo Olympics recently concluded after being delayed for over a year due to coronavirus concerns. However, with the new schedule came the increased use of artificial intelligence to the table. The use of smart tech has completely altered the way AI was used in sports and fitness. Since the sports industry requires number crunching, it makes for an ideal setting for artificial intelligence. Let's see how AI is changing the sports industry for good.

While many things in the world are unpredictable, for things that can be predicted using data, there's AI. And, the world of sports is one such quantifiable thing. The use of AI in sports has become common in recent years. And, thanks to the positive impact it has left on the fitness industry, it will only continue to stretch its arms in the realm of sports.

As per PwC research, AI-based tools are already being used in almost all major sports disciplines such as cricket, baseball, soccer, and American football alongside non-professional leisure activities such as grassroots sports. These tools include sensors, wearables, and computer vision-powered cameras that gather data on athletes' performance.

Besides, natural language processing devices can make use of speech and text recognition to gain insights into the audience's sentiments. All this data can be crunched leveraging machine learning (ML) and deep learning (DL) systems to create forecast models and enable coaches to make better decisions. Let's get into the details.

Evaluating an athlete using quantitative metrics might not tell you a lot about them, but their performances can be subject to such scrutiny. Sports organizations are using this data as a measure of fitness and the potential of the athlete. However, the data used for recruiting doesn't mean using widely known stats of the person but using more complex metrics that consider other aspects as well.

The process of gathering data has become even more easy and reliable ever since big data and AI have taken over sports management. AI can use historical data to predict the future performance of players before recruiting them. The same process goes into predicting the market values of players before making a contract offer to new talent.

The analytical and predictive capabilities of AI go beyond just recruiting new players. They also find application in medical diagnostics, which is very imperative for players' performance. AI-backed tools can check for several physical parameters such as athletes' movements to determine the condition and spot injuries before the players even realize it.

Gathering information with sensors is necessary for data analytics in healthcare, and as per the latest trends, health wearables are the best products for that due to their portability and cheap price tags.

Their ability to track biometrics makes them more popular not just among professional athletes but also for fitness enthusiasts. As per MarketsandMarkets research, wearables are the biggest and fastest-growing share in the sports device market.

Besides making things easier for players and managers, AI is also capable of revolutionizing live broadcasting and impact the way viewers experience sports. Artificial Intelligence can also alter the way broadcasters make money from sporting events.

Moreover, AI systems can come in handy to automatically choose a suitable camera angle to provide the best viewing experience possible. It can smartly provide subtitles for live sporting events in viewers' preferred languages based on their whereabouts.

AI systems can also identify the best opportunities to push advertisements based on the reaction and excitement levels of the crowd. This will enable broadcasters to effectively make money through ad sales.

As they say "applause doesn't come without caveats," the use of AI systems could have some downsides. Data trading can be used for betting purposes as there is an immense amount of financial benefits involved. NCAA signed a 10-year contract with a UK IT company to collect and sell sports data to media corporations.

Another challenge could be keeping human talent intact since AI could take over sports such as car racing where results are most influenced by non-human elements. For instance, to win an F1 race, almost everything is data-driven, including the time taken during a pit stop.

Using data collected by onboard sensors is so important that Formula One teams use Amazon-powered cloud-computing services. This information can be stored and fed to AI to come up with the best strategies to succeed. Considering the pace at which AI is stretching its reach in sports, this trend can spread to all other sports, causing a clash between human talents and AI systems.

But we mustn't forget one thing that despite using AI to make predictions, we cannot rule out unpredictability and surprise from sports by virtue of the human element. After all, that's what makes sports exciting for viewers across the globe.

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Artificial Intelligence Is Changing The Face Of Tech In ...

Top Performing Artificial Intelligence (AI) Companies of 2021

As artificial intelligence has become a growing force in business, todays top AI companies are leaders in this emerging technology.

Often leveraging cloud computing and edge computing, AI companies mix and match myriad technologies to meet and exceed use case expectations in the home, the workplace, and the greater community. Machine learning leads the pack in this realm, but todays leading AI firms are expanding their technological reach through other technology categories and operations, ranging from predictive analytics to business intelligence to data warehouse tools to deep learning, alleviating several industrial and personal pain points.

Entire industries are being reshaped by AI. RPA companies have completely shifted their platforms. AI in healthcare is changing patient care in numerous and major ways.

AI companies attract massive investment from venture capitalist firms and giant firms like Microsoft and Google that see the potential for further growth in corporate and personal use. Academic AI research is growing quickly in quantity and complexity, as are AI job openings across a multitude of industries. All of this growth and the exciting potential for new growth are documented in the AI Index, produced by Stanford Universitys Human-Centered AI Institute.

Consulting giant Accenture argues that AI has the potential to boost rates of profitability by an average of 38% and could lead to an economic boost of a whopping $14 trillion in additional gross value added (GVA) by 2035.

Especially during the COVID-19 pandemic, fields like healthcare have grown their interest and investment in AI, hoping to propel patient experiences forward in telemedicine, digital imaging, and a variety of other areas that give the patient greater access to medical resources they need.

Artificial intelligence clearly holds many possibilities, but IT professionals and other users should be cautious of a plethora of risks, such as job displacement. It will have a huge economic impact but also change society, and its hard to make strong predictions, but clearly job markets will be affected, said Yoshua Bengio, a professor at the University of Montreal, and head of the Montreal Institute for Learning Algorithms.

To keep up with the AI market, we have updated our list of top AI companies playing a key role in shaping the future of AI.

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Even during the COVID-19 pandemic where most industries reduced their total expenses to stay afloat, many companies actually increased their AI investments in 2020.

The AI vendors are leading the market by providing AI and ML through their popular cloud platforms, enabling companies to incorporate AI into applications and systems without the expense of in-house development.

The clear leader in cloud computing, AWS offers both consumer and business-oriented AI products and services, and many of its professional AI services build on the Ai services available in consumer products. Amazon Echo brings artificial intelligence into the home through the intelligent voice server, Alexa. For AWS, the companys primary AI services include Lex, a business version of Alexa; Polly, which turns text to speech; and Rekognition, an image recognition service.

Google, a leader in AI and data analytics, is on a massive AI acquisition binge, having acquired a number of AI startups in the last several years. Google is deeply invested in furthering artificial intelligence capabilities. In addition to using AI to improve its services, Google Cloud sells several AI and machine learning services to businesses. It has an industry-leading software project in TensorFlow, as well as its own Tensor AI chip project.

IBM has been a leader in the field of artificial intelligence since the 1950s. Its efforts in recent years center around IBM Watson, an AI-based cognitive service, AI software as a service, and scale-out systems designed for delivering cloud-based analytics and AI services. It has been acquisitive, purchasing several AI startups over several years. It benefits from having a strong cloud platform.

Microsoft offers a mix of consumer-facing and business/IT AI projects. On the consumer side, it has Cortana, the digital assistant that comes with Windows and is now available for smartphones other than Windows Phone, and the chatbot Zo that talks like a teenager. On its Azure cloud service, Microsoft sells AI services such as bot services, machine learning, and cognitive services.

The leading cloud computing platform in Asia, Alibaba offers clients a sophisticated Machine Learning Platform for AI. Significantly, the platform offers a visual interface for ease of use, so companies can drag and drop various components into a canvas to assemble their AI functionality. Also included in the platform are scores of algorithm components that can handle any number of chores, enabling customers to use pre-built solutions. Expect huge AI growth from Alibaba in the years to come.

These top AI providers are demonstrating that artificial intelligence can be used in a dazzling number of ways, across virtually every industry sector.

Palmer Luckey is one of the most intriguing figures in todays emerging tech. He co-founded Oculus, which Facebook bought for a cool $2 billion in 2014. Post-Facebook and at the ripe age of 27, he launched Anduril, which adds sophisticated sensors, vehicles, and drones to create a threat protection zone. Products include Sentry Tower (autonomous awareness), Ghost 4 sUAS (intelligent air support), and Anvil sUAS (precision kinetic intercept).

Formerly known as Sift Science, the company provides multiple online fraud management services in one platform. Sift mines thousands of data points from around the web to train in detecting fraud patterns. Its machine learning tools, bolstered by data analytics, seek insight into fraud before it happens.

Nauto offers an AI-powered driver behavior learning platform. So instead of self-driving cars, Nauto is an AI technology designed to improve the safety of commercial fleets and autonomous fleets. The platform assesses how drivers interact with the vehicle and the road ahead to reduce distracted driving and prevent collisions.

Tempus data-driven precision medicine uses AI to fight disease and bolster patient outcomes. It gathers and analyzes massive pools of medical and clinical data at scale to provide precision medicine that personalizes and optimizes treatments to each individuals specific health needs. Applications include neurology, psychiatry, and oncology.

In recent years, Salesforce has acquired a handful of AI companies and sharpened features of Salesforce Einstein, their artificial intelligence service. Their latest initiative, which includes an extensive team of data scientists, uses machine learning to help employees more efficiently perform tasks by simplifying and speeding them up. In addition to Salesforces employees, Einstein is available for customers who can build their own applications and are interested in features like Recommendation Builder, scorecards, and in-depth navigation insights.

A dominant vendor in the small but growing Robotic Process Automation market it actually coined the term RPA Automation Anywhere makes great use of AI. Its applications include attended RPA, which helps office employees do mundane, repetitive tasks much more efficiently, employing the power of machine learning. A vendor to watch.

SenSat builds digital copies of physical environments and applies AI modeling to understand the parameters of that environment and provide valuable feedback. For example, it can give spatial and volume statistics about a roadway that is about to undergo repair work. Boosting its fortunes, in October 2019, Tencent led a $10 million investment in SenSat.

Phrasee specializes in natural language generation for marketing copy. Its natural language generation system can generate millions of human-sounding variants of marketing at the touch of a button, allowing customers to tailor their copy to targeted customers. Retail/marketing and AI is a combination on a rapid growth curve in the AI sector. During the COVID-19 pandemic, several retailers, such as Walgreens, used Phrasee to boost customer engagement related to vaccination.

Using a combination of human freelancers and a system built with machine learning automation, Defined Crowd provides a data set that companies can leverage to improve the performance of their algorithms. This union of the human with AI is a brilliant stroke other startups are catching on, and you can expect many more startups to test out this combo.

Based in New York City, Pymetrics leverages AI to help companies hire the optimal candidates, by examining more than a resume scan. Customers have their best employees fill out the Pymetrics assessment, which then creates a model for what future ideal candidates should bring to the table. In essence, the AI-based system is attempting to find more new staff that will fit in well with the existing top staff, using AI and behavioral science.

Siemens, the famed legacy German multinational, focuses on areas like energy, electrification, digitalization, and automation. They also work to develop resource-saving and energy-efficient technologies and are considered a leading provider of devices and systems for medical diagnosis, power generation, and transmission. Yes, the Siemens website actually refers to AI at the beer garden.

Given how lucrative it is for hackers, will identity theft ever go away? Its unlikely, but New York City-based Socure is using AI to fight it. Its AI-enabled system monitors and checks the quality of countless data sources far more than a human, of course, but more importantly, far more than a legacy system that doesnt have the speed, flexibility, and insight of AI. Its motto is identify more real people in real-time. Socure was named a Cool Vendor 2020 in Gartners Cool Vendors in AI for Banking and Investments.

AEye builds the vision algorithms, software, and hardware used to guide autonomous vehicles. Its LiDAR technology focuses on the most important information in a vehicles sightline, such as people, other cars, and animals, while putting less emphasis on other landscape features like the sky, buildings, and surrounding vegetation. In February 2021, AEye entered into a merger agreement with CF Finance Acquisition Corp. III, so if/when the deal closes, expect more investment and innovation in the near future.

In a world with a vast ocean of podcasts and videos to transcribe, Rev uses AI to find its market. An AI-powered but human-assisted transcription provider, the company also sells access to developers, so tech-savvy folks can use its speech recognition technology. But the key part here is the combination of humans with AI, which is a sweet spot in the effective use cases for artificial intelligence. With a growing need for accessibility features in audiovisual production especially, expect more AI companies to take advantage of a similar business model in the future.

Its not enough that Suki offers an AI-powered software solution that assists doctors as they make voice notes on a busy day. Sukis aim using the power of AI to learn over time is to mold and adapt to users with repeated use, so the solution becomes more of a time saver and efficiency booster for physicians and healthcare workers over time. As a sign of the times, Suki was delivered with COVID-19 data and templates to speed the critically important vaccination and health tracking processes.

In the future, everything will be tracked by intelligent cameras. Verkada is working to create that future by offering a network of AI-assisted cameras that can handle sophisticated movement monitoring, through a software-first approach to security. Given all the uses for such cameras, which employ the cloud, its no surprise that the companys clients range from schools to shopping malls.

DataVisor uses machine learning to detect fraud and financial crime, utilizing unsupervised machine learning to identify attack campaigns before they result in any damage. DataVisor protects companies from attacks such as account takeovers, fake account creation, money laundering, fake social posts, fraudulent transactions, and more.

Founded in 2016, People.ais goal is to streamline the life of salespeople, assisting them in putting the reams of small details into relevant CRM systems, chiefly Salesforce. Think of all those pesky info bits from texting, your calendar, endless Slack conversations People.ai aims to help you with all of that. Plus: the system attempts to coach sales reps on the most effective ways to manage their time.

AlphaSense is an AI-powered search engine designed for investment firms, banks, and Fortune 500 companies. The search engine focuses on searching for important information within earnings call transcripts, SEC filings, news, and research. The technology also uses artificial intelligence to expand keyword searches for relevant content.

The remarkable truth about AI is that it keeps moving up the food chain in terms of the sophisticated tasks it can handle. Taking a big step up from simple automation, Icertis with a decade under its belt handles millions of business contracts through a method they call contract intelligence. Leveraging the cloud, the companys solution automates certain tasks and scans previous contract details. The company has gained some big clients like Microsoft and has been named a Gartner 2020 Leader.

Casetext is an AI-powered legal search engine that specializes in legal documents, with a database of more than 10 million statutes, cases, and regulations. A recent study comparing legal research platforms found that attorneys using Casetexts CARA AI finished their research more than 20% faster, required 4.4 times fewer searches to accomplish the same research task, and rated the cases they found as significantly more relevant than those found with a legacy research tool.

Blue River Technology is a subsidiary of Deere & Co. that combines artificial intelligence and computer vision to build smart farm tech clearly a growing need, given population growth. The companys See & Spray technology can detect individual plants and apply herbicide to the weeds only. This reduces the number of chemicals sprayed by up to 90% over traditional methods.

Nvidias emergence as an AI leader was hardly overnight. It has been promoting its CUDA GPU programming language for nearly two decades. AI developers have come to see the value in the GPUs massively parallel processing design and embraced Nvidia GPUs for machine learning and artificial intelligence. One area Nvidia is making a big push is in self-driving cars, but it is one of many efforts on the horizon.

Automation in factories has been progressing for years, even decades, but Bright Machines is working to push it a quantum leap forward. Based in San Francisco, the AI company is leveraging advances in robotics like machine learning and facial recognition to create an AI platform for digital manufacturing. Its solutions can accomplish any number of fine-grain tasks that might previously have required the exactitude of a skilled human.

Orbital Insight uses satellite geospatial imagery and artificial intelligence to gain insights not visible to the human eye. It uses data from satellites, drones, balloons, and other aircraft to look for answers or insight on things related to the agriculture and energy industries that normally wouldnt be visible. The company touts itself as the leader in geospatial analytics.

Once a standalone company and now a division of MasterCard, Brighterion offers AI for the financial services industry, specifically designed to block fraud rates. The companys AI Express is a fast-to-market solution within 6-8 weeks that is custom designed for customer use cases. Its solution is used by the majority of the 100 largest banks.

H2O.ai provides an open-source machine learning platform that makes it easy to build smart applications. Used by many thousands of data scientists across a large community of organizations worldwide, H2O claims to be the worlds leading open-source deep learning platform. H20.ai provides solutions for insurance, healthcare, telecom, marketing, financial service, retail, and manufacturing.

With a long legacy as the top chipmaker, Intel has both hardware and software AI initiatives in the works. Its Nervana processor is a deep learning processor, while Movidius is geared toward neural networks and visual recognition. Intel is also working on natural language processing and deep learning through software and hardware. Further indicating their commitment to AI, one of the companys slogans is accelerate your AI journey with Intel.

Clarifai is an image recognition platform that helps users organize, filter, and search their image database. Images and videos are tagged, teaching the technology to find similarities in images. Its AI solution is offered via mobile, on-premise, or API. Beyond image recognition, Clarifai also offers solutions in computer vision, natural language processing, and automated machine learning.

Geared to assist the busiest of people, X.ais intelligent virtual assistant Amy helps users schedule meetings. The concept is simple if you receive a meeting request but dont have time to work out logistics, you copy Amy onto the email and she handles it. Through machine learning and natural language processing, Amy schedules the best time and location for your meeting based on your preferences and schedule. We all need a helper like this in our lives.

Zebra Medical Systems is an Israeli company that applies deep learning techniques to the field of radiology. It claims it can predict multiple diseases with better-than-human accuracy by examining a huge library of medical images and specialized examination technology. It recently moved its algorithms to Google Cloud to help it scale and offer inexpensive medical scans.

Iris.AI helps researchers sort through cross-disciplinary research to find relevant information, and as it is used more often, the tool learns how to return better results. Since its launch, countless people have tried the service, some becoming regular users. Its Iris.AI release includes the Focus tool, an intelligent mechanism to refine and collate a reading list of research literature, cutting out a huge amount of manual effort.

Freenome uses artificial intelligence to conduct cancer screenings and diagnostic tests to spot signs of cancer earlier than possible with traditional testing methods. It uses non-invasive blood tests to recognize disease-associated patterns. The companys solution has trained on cancer-positive blood samples, which enable it to detect problems using specific biomarkers.

Neurala claims that it helps users improve visual inspection problems using AI. It develops The Neurala Brain, a deep learning neural network software that makes devices like cameras, phones, and drones smarter and easier to use. AI tends to be power-hungry, but the Neurala Brain uses audio and visual input in low-power settings to make simple devices more intelligent.

Graphcore makes what it calls the Intelligence Processing Unit (IPU), a processor specifically for machine learning, used to build high-performance machines. The IPUs unique architecture allows developers to run current machine learning models orders of magnitude faster and undertake entirely new types of work not possible with current technologies.

CognitiveScale builds customer service AI apps for the healthcare, insurance, financial services, and digital commerce industries. Its products are built on its Cortex-augmented intelligence platform for companies to design, develop, deliver, and manage enterprise-grade AI systems. It also has an AI marketplace, which is an online AI collaboration system where business experts, researchers, data scientists, and developers can collaborate to solve problems.

iCarbonX is a Chinese biotech startup that uses artificial intelligence to provide personalized health analyses and health index predictions. It has formed an alliance with seven technology companies from around the world that specialize in gathering different types of healthcare data and will use algorithms to analyze genomic, physiological, and behavioral data. It also works to provide customized health and medical advice.

Human Resources can be a bifurcated digital workspace, with different apps for each task that HR handles. OneModel is a talent analytics accelerator that helps HR departments handle employees, career pathing, recruiting, succession, exits, engagement, surveys, HR effectiveness, payrolls, planning, and other HR features all in one place and in a uniform way. The companys core goal is to equip HR pros with machine learning smarts.

AI meets social media. Lobster Media is an AI-powered platform that helps brands, advertisers, and media outlets find and license user-generated social media content. Its process includes scanning major social networks and several cloud storage providers for images and video, using AI-tagging and machine learning algorithms to identify the most relevant content. It then provides those images to clients for a fee.

Next IT, now part of Verint, is one of the pioneers in customer service chatbots. It develops conversational AI for customer engagement and workforce support on any endpoint through intelligent virtual assistants (IVAs). The companys Alme platform powers natural language business products that are continually enhanced through AI-powered tools that empower human trainers to assess performance and end-user satisfaction.

Pointr is an indoor positioning and navigation company with analytics and messaging features that help people navigate busy locations, like train stations and airport terminals. Its modules include indoor navigation, contextual notifications, location-based analytics, and location tracking. Its Bluetooth beacons use customer phones to help orient them around the building.

One of the largest social media companies to come out of China, Tencent has an advanced AI lab that developed tools to process information across its ecosystem, including natural language processing, news aggregators, and facial recognition. They also have one of Chinas top video streaming platforms, Tencent Music. A giant in the field, they fund several AI efforts.

A fairly new startup in the AI copywriting space, Copy.ai uses basic inputs from users to generate marketing copy in seconds. It can create copy for a variety of different formats, including article outlines, meta descriptions, digital ads and social media content, and sales copy. In March 2021, it was announced that Copy.ai raised $2.9 million in investments from Craft Ventures and several other smaller investors. With its use of the GPT-3 language model to generate words, Copy.ai is a content-driven AI tool to keep an eye on.

Twilio is a cloud communications platform as a service (PaaS) company that allows software developers to integrate text messages, phone calls, and video calls into applications through the use of various APIs. Twilios services are accessed over HTTP and are billed based on usage. The Twilio Autopilot offering allows companies to build and train AI-driven chatbots.

ViSenzes artificial intelligence visual recognition technology works by recommending visually similar items to users when shopping online. Its advanced visual search and image recognition solutions help businesses in eCommerce, mCommerce, and online advertising by recommending visually similar items to online shoppers.

Based in Asia, SenseTime develops facial recognition technology that can be applied to payment and picture analysis. It is used in banks and security systems. Its valuation is impressive, racking several billion dollars in recent years. The company specializes in deep learning, education, and fintech.

Using machine learning to mine health data for cancer research, Flatiron finds cancer research information in near real-time, drawing on a variety of sources. The company raised more than $175 million in Series C funding before being acquired by cancer research giant Roache.

Deep 6 uses AI to, in its own words, find more patients in minutes, not months. The patients in this sense are participants in clinical trials a critical part of the research process in developing new medicine. Certainly one of the challenging issues that was faced during the quest for a COVID-19 vaccine was finding a community of appropriate candidates. Deep 6 finds these kinds of communities by using an AI-powered system to scan through medical records, with the ability to understand patterns in human health.

Considered one of the best AI-driven customer support tools out there, Directly counts Microsoft as a customer. It helps its customers by intelligently routing their questions to chatbots to answer their questions personally, or to customer support personnel. It prides itself on intelligent automation.

Based in Montreal, Element AI provides a platform for companies to build AI-powered solutions, particularly for firms that may not have the in-house talent to do it. Element AI says it supports app-building for predictive modeling, forecasting modeling, conversational AI and natural language processing, image recognition, and automatic tagging of attributes based on images. The company was founded in 2016.

Pony.ai develops software for self-driving cars and was created by ex-Google and Baidu engineers who felt that the big companies are moving too slow. It has already made its first fully autonomous driving demonstration. It now operates a self-driving ride-sharing fleet in Guangzhou, China, using cars from a local automaker. The company raised $400 million from Toyota.

Focusing on enterprise AI, C3.ai offers a wide array of pre-built applications, along with a PaaS solution, to enable the development of enterprise-level AI, IoT applications, and analytics software. These AI-fueled applications serve a wide array of sectors and industry verticals, from supply chains to healthcare to anti-fraud efforts. The goal is to speed and optimize the process of digital transformation.

Some of the best applications of AI look into the future to prevent future problems. Such is the goal with BigPanda, which leverages AI to lessen or stop IT outages before they take down a full business, an eCommerce operation, or a mission-critical application. In essence, this companys goal is the magic of AIOps, using AI to improve admin and IT operations. A major growth area.

Accubits, a top-rated AI development company, focuses most of its energy on helping businesses enable AI for new efficiencies in their existing systems. Some of their AI solutions include intelligent chatbots in CRMs and predictive health diagnostics, both of which are designed to mesh with your existing software infrastructure. Accubits works across industries like consumer technology, automotives, cybersecurity, healthcare, and fashion.

Stem is a veteran energy storage firm that has adopted AI to help automate energy management. It uses its industry-leading AI platform, Athena, to determine when to charge energy storage systems and when to draw on them. Athena focuses on energy forecasting and automated control.

The robots imagined by 1950s futurists were tin men that could walk and talk and probably become masters of the human race. It hasnt turned out that way (fortunately), but Bossa Nova Robotics is using AI to make todays robots more effective. Indeed, modern robots are rarely shaped like humans; Bossa Novas robots resemble tall vacuum cleaners. Ironically, Bossa Nova started as a robotic toymaker but now has full-scale robots in retailers like Walmart. The robots roll up and down the shelves, spotting inventory problems and allowing cost savings on human workers.

In a world run by data, in many cases, someone or some system has to prep that data so that its usable. Data prep is unglamorous but absolutely essential. Tamr combines machine learning and human tech staff to help customers optimize and integrate the highest value datasets into its operations. Referred to as an enterprise-scale data unification company, Tamr enables cloud-native, on-premise, or hybrid scenarios truly a good fit for todays data-driven, multi-cloud world.

Formerly known as InsideSales.com, Xant underwent a major rebrand and now focuses on the enterprise market. It is a sales acceleration platform with a predictive and prescriptive self-learning engine, assisting in a sale and providing guidance to the salesperson to help close the deal. At its core is machine learning.

Dataminr is a global real-time information discovery company that monitors news feeds for high-impact events and critical breaking news far faster than your Google newsfeed. It cuts through the clutter of non-news or irrelevant news to specific industries and only provides highly relevant news when it happens. For news-sensitive vendors, its goal is to detect early risks from media coverage.

Theres a gray area in our lives in terms of healthcare; we ask ourselves, does this problem Im having really require making a doctors appointment, or could a major dose of simple information be enough? K Healths AI solution operates in this area. Users can text with a doctor or find similar cases near them, which has been particularly useful for COVID-19. Using a model built from a vast store of anonymous health records, its system offers help based on how a users complaint correlates with this vast history of other patients. Think of K Health as the advanced edge of telemedicine.

Driving the AI revolution with the highly capable smartphone chips it makes, Qualcomm leverages a signal processor for image and sound capabilities. In March 2021, Qualcomm acquired NUVIA, a competitive CPU and technology design company, ultimately enhancing CPU opportunities for the future. Given its market size and power, its likely that Qualcomm will continue to be a key driver of AI functionality in the all-important consumer device market.

HyperScience is designed to cut down on the tedium of mundane tasks, like filling out forms or data entry of hand-written forms. It also processes the relevant information from forms rather than requiring that a human read through the whole form. It touts itself as intelligent document processing.

Vivints Smart Home is a popular smart home service in North America, with features like security cameras, heating and cooling management, door and window security, and a remote speaker to talk to people at the door. All of this is monitored by AI, which learns the residents behavioral patterns and adjusts management accordingly.

While Facebook is certainly better known in other areas as one of the largest social media networks in the world, the company is making great strides in its AI capabilities, especially in self-teaching for its newsfeed algorithms. Most significantly, the Facebook team has started using AI to screen for hate speech, fake news, and potentially illegal actions across posts on the site.

Symphony Ayasdi is a machine intelligence software company that offers intelligent applications to its clients around the world for using Big Data and complex data analytics problems. Its goal is to help customers automate what would be the manual processes of using their own unique data. In March 2021, Symphony AyasdiAI announced a new partnership with Sionic, leading to a greater focus on financial crime detection. Very much focused on the enterprise AI sector.

A well-known technology company in the contract world, DocuSign uses esignature technology to digitize the contracting process across a multitude of industries. Many users dont realize some of the AI features that DocuSign powers, such as AI-powered contract and risk analysis that gets applied to a contract before you sign. This AI process lends itself to more efficient contract negotiation and/or renegotiations.

This cloud-based SaaS firm focuses on endpoint security. Leveraging AI, CrowdStrikes Falcon platform enables it to identify what it calls active indicators of attack to detect malicious activity before a breach actually happens. It presents the network administrators with actionable intelligence of real-time findings for them to take necessary action.

Cylance, now a division of BlackBerry, develops security apps that prevent instead of reactively detecting viruses and other malware. Using a mathematical learning process, Cylance identifies what is safe and what is a threat rather than operating from a blacklist or whitelist. The company claims its machine learning has an understanding of a hackers mentality to predict their behavior.

Tetra Tech uses AI to take notes on phone calls, so people working in call centers can focus on discussions with the callers. It uses AI to generate a detailed script of dialogues using its speech recognition technology. Given the large market for call centers and the need to make them more effective at low cost this is a big market for AI.

Nuro makes very small self-driving electric delivery trucks designed for local deliveries, such as groceries or takeout. Its founders previously worked on Googles Waymo self-driving car project. Overall the companys goal is to boost the value of robotics in daily life.

SoundHound started as a Shazam-like song recognition app called Midomi, but it has expanded to answering complex voice prompts like Siri and Cortana. But instead of converting language into text like most virtual assistants, the apps AI combines voice recognition and language understanding into a single step.

Acquired in a $1.2 billion high profile deal by Amazon, Zoox is focused on self-driving cars or, in the larger sense, a self-driving fleet (hence Amazons interest). Their AI-based vehicle is geared for the robo-taxi market.

Founded in 2013, AI biotech company Zymergen describes itself as a biofacturer. One of their offerings is called Hyline, a bio-based polyimide film. Their work includes applications for pharmaceuticals, agriculture, and industrial uses. Based in Emeryville, California.

A company designed to help digital advertisers run targeted digital advertising campaigns, The Trade Desk uses AI to optimize its customers advertising campaigns for their appropriate audiences. Their AI, known as Koa, was built to analyze data across the internet to figure out what certain audiences are looking for and where ads should be placed to optimize reach and cost. The Trade Desk also allows you to launch your digital ads independently, but uses its AI to offer performance suggestions while your campaign is live.

Based in China, DJI is a big player in the rapidly growing drone market. The company is leveraging AI and image recognition to track and monitor the landscape, and its expected that the company will play a role in the self-driving car market. Impressively, DJI has partnered with Microsoft for a drone initiative.

Running AI is exceptionally data-intensive the more data the better and so todays chipmakers (like Intel and Nvidia) are star players. Add to that list HiSilicon. The company fabricated the first AI chip for mobile units. Impressively, the chip accomplishes tasks like high-speed language translation and facial recognition.

Insitro operates at the convergence of human biology and machine learning. More specifically, it uses artificial intelligence to build models of various human illnesses, using those models to forecast previously unknown solutions far beyond human intuition. These models use the power of ML to improve drug discovery and development. Founded by Daphne Koller, Insitro has drawn investment from an exhaustive array of VC and financial firms.

A leading RPA company, Blue Prism uses AI-fueled automation to do an array of repetitive, manual software tasks, which frees human staff up to focus on more meaningful work. The companys AI laboratory researches automated document reading and software vision. To further boost its AI functionality, Blue Prism bought Thoughtonomy, which has AI based in the cloud.

You have surely encountered the limited conversational elan of a chatbot; a few stock phrases delivered in a monotone. Rulai is working to change this using the flexibility and adaptability of AI. The company claims its level 3 AI dialog manager can create multi-round conversation, without requiring code from customers. Clearly a major growth area.

Think of these forward-looking AI companies as taking a particularly inventive approach to machine learning and AI.

OpenAI is a non-profit research firm that operates under an open-source type of model to allow other institutions and researchers to freely collaborate, making its patents and research open to the public. The founders say they are motivated in part by concerns about existential risk from artificial general intelligence.

With backing by some real heavyweights Jeff Bezos, Elon Musk, and Mark Zuckerberg Vicariouss goal is nothing less than to develop a robot brain that can think like a human. It hasnt been particularly forthcoming with details, but its AI robots, geared for industrial automation, are known to learn as they do more tasks.

Arguably the coolest application of AI on this entire list, Ubiquity6 has built a mobile app that enables augmented reality for several people at once. Users see and interact with objects presented by the fully dimensioned visual world of the Ubiquity app, immersing themselves in a creative or educational environment. The companys website is worth visiting for its visual creativity and wonderment alone.

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Top Performing Artificial Intelligence (AI) Companies of 2021

Elon Musk unveils plans for humanoid robot that uses Tesla …

Tesla Inc. showcased its artificial-intelligence systems on Thursday amid renewed criticism for Autopilot, its most-talked-about AI-based system, as it unveiled its next big project: a humanoid robot.

At the companys first AI Day, Chief Executive Elon Musk gave a preview of the Tesla Bot, a 5-foot-8-inch robot with a screen for a face, weighing about 125 pounds and capable of moving about 5 mph slow enough for people to run away from and small enough so a human could overpower it, Musk joked. He said a prototype is expected next year.

Musk said building a humanoid robot is a logical next step for Tesla, since, he said, its already the worlds biggest robotics company, with its cars basically robots. The humanoid robot will use all the tools in Teslas vehicles sensors, cameras, neural networks, etc. to autonomously navigate the outside world.

Were making the pieces that would be useful for a humanoid robot, so we should probably make it. If we dont, someone else will and we want to make sure its safe, Musk said.

I think this will be quite profound, Musk added, speculating that the robot could eventually change how the world works. While it could be used for things as basic as household chores, its intended for unsafe, repetitive or boring tasks, he said. Basically, what is the work people would least like to do?

In the future, physical labor will be a choice, Musk said, adding that that will likely result in a universal basic income, someday.

Musk said he hopes the robot is not perceived as something dystopian, and that it could even be your friend. When asked in a Q&A session about the possibility of superhuman AI eventually running amok, Musk said that while thats a concern, Tesla is aiming to make useful, or narrow, AI that will be used unequivocally for good.

When asked how Tesla expects to find a consumer market for the robot, Musk quipped Well, youll just have to see.

Earlier in the presentation, Andrej Karpathy, director of Artificial Intelligence and Autopilot Vision at Tesla, delivered a highly technical explanation of Teslas neural network, aka the brain of Teslas vehicles, and laid out in detail how Tesla uses cameras and AI for predictive learning. Other Tesla AI executives laid out Teslas technological breakthroughs in labeling data and building a super-fast computer to train Autopilot.

In addition to showing off its technology, AI Day served as a recruitment event, and Musk encouraged prospective hires to apply for hardware or software jobs at Tesla. Join our team, help build this, he said.

The event came as federal regulators have launched another investigation into Autopilot, Teslas advanced driver-assistance system, following several crashes involving parked emergency-response vehicles and emergency scenes.

Tesla shares TSLA, +0.38% ended the regular trading day down 2.3%, and edged up 0.4% in Fridays premarket session.

The National Highway Traffic Safety Administration also has opened several investigations related to Autopilot, including some that resulted in deaths. Alleged Autopilot malfunctions have sparked more than a dozen lawsuits in the U.S.

On Monday, two U.S. senators called on the Federal Trade Commission to look into whether Tesla misleads consumers by overstating Autopilots capabilities.

Tesla has long said Autopilot makes driving safer, and that it makes it clear that drivers have to be alert and prepared to take over at any time upon engaging Autopilot. Musk said during Thursdays presentation that Autopilots basic purpose is to avoid crashes, and said it does that very well.

Dont miss: Opinion: Its time for Elon Musk to start telling the truth about autonomous driving

For equally long, however, critics have said the system gives some drivers a false sense of security and implies self-driving abilities well beyond its real-world capabilities.

Musk is known for his bold claims that dont always pan out, such as promising for years a completely autonomous Los Angeles-to-New York trip, and plans for a fleet of robotaxis as early as this year.

Musk first announced the idea of holding an AI Day on Twitter in June, saying it would help with Teslas recruiting efforts.

Tesla has held similar special events in the past, including ones to unveil new vehicles or new vehicle trims, like the one held in June to reveal the Model S Plaid, and another to highlight its battery technology in September 2020.

Tesla stock has lost about 4% this year, contrasting with gains of more than 17% for the S&P 500 index SPX, +0.22%.

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Elon Musk unveils plans for humanoid robot that uses Tesla ...

10 Famous People in Artificial Intelligence

Artificial intelligence is rapidly excelling at many human tasks, such as medical diagnosis, language translation, and customer support. This is creating legitimate concerns that AI will eventually displace human employees throughout the economy. But it is hardly the only, or even the most likely, consequence. Never once have digital technologies been so attentive to us, nor have we been so reactive to our devices. As AI will fundamentally transform how and who performs work, the technologys greater effect will be in complementing and boosting human talents rather than replacing them.

The AI revolution has already started, and it is changing every area of our life.

From academics and researchers to inventive tech entrepreneurs, here are 10 people pushing the frontiers of deep learning and computerized vision to make AI dreams come true.

Musks emphasis at Open AI, his non-profit organisation, is on creating safe artificial general intelligence (AGI) in a manner that enhances humanity, despite the possible risks of AI. They do groundbreaking research and develop open-source tools for experimentation, such as OpenAI Universe.

Musk wants to create a neural implant that connects the human brain to artificial intelligence, allowing people to operate computers, artificial limbs, and other equipment with their thoughts alone.

Schjll Bredes objective with her AI scientist, Iris.ai, is to speed up research. The technology searches through over 60 million papers for the most relevant articles using algorithms. Her goal isnt to make money, but to do good and improve peoples lives, such as by discovering a cancer treatment.

Schmidhuber is known as the Father of Self-Aware Robots for developing the mechanisms that allow us to communicate to our phones. His current research focuses on developing artificial neural networks that are equivalent to, and eventually surpass, the human brain.

Li, a well-known computer vision professor, is on a mission to democratise AI to guarantee that talent and expertise are shared outside large corporations in order to increase diversity, creativity, and innovation. AI4ALL, her non-profit, trains the next generation of AI technologists, philosophers, and entrepreneurs.

Selzs firm, Squirro, is the pioneer in real contextual intelligence, promoting data as the worlds most expensive resource. Artificial intelligence and machine learning are used to offer the why behind data, resulting in actionable intelligence and a deep knowledge of customers.

Socher has been dubbed one of the artificial intelligence and machine learning spaces virtuosos. By providing a deeper grasp of context and mood, his methods are revolutionising natural language processing and computer vision.

Hassabis, dubbed the superhero of artificial intelligence, is advancing AI by reuniting it with cognitive science. He works at DeepMind on innovative algorithms that can help mankind in areas like healthcare and environmental issues.

Cucchiara has almost 300 publications to his credit. Her work in computer vision, multimedia recognition systems, machine learning, smart sensing, and human behaviour understanding (HBU) has had a significant impact in assisting businesses in pushing AI limits.

Sensorbeat, the first comprehensive package comprising all the electronic devices and AI for interpreting the motion of items and people, was conceived and developed by Hardebrings team. Real-time applications are enabled by smart data processing embedded into the gadget itself.

Li wants to be in charge of AIs destiny in China and abroad. Baidu is constantly engaged in cutting-edge research in fields such as artificial intelligence, computer vision, and machine learning. Baidu Brain is a set of AI-powered solutions, as well as DuerOS, a smart speaker platform.

One of the most often mentioned advantages of AI technology is automation, which has had substantial effects on the communication, transport, consumer goods, and service sectors. Automation not only provides for higher production levels and efficiency in various industries, but it also allows for more efficient input materials usage, better product quality, shorter lead times, and improved safety. Automation can also assist to free up resources that can be put to better use.

Artificial Intelligence has long been used to help businesses make better decisions. To make good judgments for the organisation, AI technology can coordinate data supply, evaluate trends, establish data consistency, give predictions, and quantify uncertainties. AI will stay neutral on the subject at hand as long as it is not trained to replicate human emotions, and it will assist in making the best choice to promote corporate efficiency.

AI-powered solutions can assist organisations in swiftly responding to consumer questions and concerns and resolving issues. Chatbots that combine conversational AI and Natural Language Processing technology may provide highly customised messages to consumers, assisting in the discovery of the optimal solution for their requirements. AI technologies can also assist customer care representatives feel less stressed, resulting in increased productivity.

Artificial Intelligence (AI) technologies are growing progressively popular in the healthcare industry. For example, remote patient monitoring technology enables healthcare practitioners to swiftly make clinical diagnosis and prescribe treatments without having the patient attend the hospital in person. AI can also help track the course of infectious diseases and even forecast their impacts and consequences in the future.

Artificial intelligence (AI) and machine learning (ML) may be used to evaluate data considerably more quickly. It can aid in the development of prediction models and algorithms for data processing and understanding the possible outcomes of various trends and events. Furthermore, AIs powerful computational skills may expedite the processing and interpretation of data for research and innovation that would otherwise take much longer for humans to examine and comprehend.

While many businesses have utilised AI to automate operations, those who use it primarily to replace workers will only experience short-term productivity improvements. In our 1,500-company study, we discovered that when people and robots collaborate, organisations make the most substantial performance gains. Humans and AI actively improve each others complimentary qualities through cooperative intellectual ability.

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10 Famous People in Artificial Intelligence