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A study, Global Artificial Intelligence (AI) in Supply Chain Market 2019, showcased current AI in supply chain market size, drivers, trends, opportunities, challenges and other segments. In addition, it also explains various definitions and classification of the artificial intelligence in supply chain industry, applications and chain structure.

In continuation of the data, the report covers various marketing strategies followed by key players and distributors, explaining AI in supply chain marketing channels, potential buyers and development history. The intent of global of the report is to depict the information to the user regarding AI in the supply chain market forecast and dynamics for upcoming years.

The report lists the essential elements that influence the growth of AI in the supply chain industry as well as wise and application wise consumption figures. In addition, the report sheds light on the technological evolution, tie-ups, acquisition, innovated business approaches and R&D statuses.

The Artificial Intelligence (AI) In Supply Chain study also incorporates new investment feasibility analysis of Artificial Intelligence (AI) In Supply Chain. Together with strategically analyzing the key micro markets, the report also focuses on industry-specific drivers, restraints, opportunities, and challenges in the Artificial Intelligence (AI) In Supply Chain market.

Moreover, the report organizes to provide essential information on current and future Artificial Intelligence (AI) In Supply Chain market movements, organizational needs and Artificial Intelligence (AI) In Supply Chain industrial innovations. Additionally, the complete Artificial Intelligence (AI) In Supply Chain report helps the new aspirants to inspect the forthcoming opportunities in the Artificial Intelligence (AI) In Supply Chain industry. Investors will get a clear idea of the dominant Artificial Intelligence (AI) In Supply Chain players and their future forecasts.

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Use of Artificial Intelligence in the Supply Chain is Expected to Grow - Supply and Demand Chain Executive

Roland Barthes was speaking metaphorically when he suggested in 1967 that the birth of the reader must be ransomed by the death of the author. But as artificial intelligence takes its first steps in fiction writing, it seems technology may one day start to make Barthes metaphor all too real.

AI is still some way off writing a coherent novel, as surreal experiments with Harry Potter show, but the future isnt so far away in Hollywood. According to Nadira Azermai, whose company ScriptBook is developing a screenwriting AI: Within five years well have scripts written by AI that you would think are better than human writing.

Self-promotion aside, if there is the possibility of a decent screenplay from ScriptBooks AI within five years, then a novel composed by machines cant be far behind. But its hard to shake the impression that, even if such novels eventually turn out to be better than human writing, something would be lost.

Perhaps the feeling comes from an idea that would be anathema to Barthes: the idea of literature as communication.

If a book is a heart that only beats in the chest of another, as Rebecca Solnit suggests, then it seems two parties are required: someone to write and someone to read. So when AI writes fiction there seems to be a missing piece, a void at the heart of the text where meaning should reside.

Barthes would have none of this, of course, insisting that it is language which speaks, not the author. In terms which strikingly anticipate the workings of software currently at the cutting edge of artificial writing, such as OpenAIs GPT-2, he argues that a text is not a line of words releasing a single meaning (the message of the Author-God), but instead a tissue of citations, resulting from the thousand sources of culture. The writer can only imitate a gesture forever anterior, never original, Barthes continues. If he wants to express himself the internal thing he claims to translate is itself only a readymade dictionary whose words can be explained (defined) only by other words, and so on ad infinitum.

And he must be on to something. Imagine yourself, some years in the future, pulling a novel by an unknown author off the shelves and finding that it is really good. Would you be any less moved by the story if you were then told it had been produced using groundbreaking AI? If all you had were the words in front of you on the page, how would you even know? Those who scoff at the idea that AI could ever pass this literary Turing test havent been paying attention for the past 50 years. Computers can now drive cars, recognise faces, translate between languages, fill in as your personal assistant, even beat the world champion at Go achievements that are often dismissed as just computation even though an expert of the 1970s would have classed any one of them as a signature ability of human intelligence.

Should publishers decide the future of literature is written in code, there may still be some hope for authors. A shift to AI-generated novels could only ever be a short-term strategy. As Barthes intuited and OpenAIs latest algorithm demonstrates, its certainly possible to assemble writing from other writing. But even if this patchwork prose becomes better than human writing, it would be only drawing on a finite well of inspiration. Train your AI on the sum total of human literature thus far and all youll get is a mass of references: a gesture forever anterior, never original. No one who witnessed the phenomenon that was the Fifty Shades of Grey series could doubt that imitation can be lucrative for a while. But when even an imitator as skilful or as lucky as EL James finds her sales on a downward curve its clear that no matter how feisty your stallion at first appears, flogging it will only get you so far.

Barthes belief in the primacy of the word, his dogged insistence that life can only imitate the book, leaves his recipe for literature missing a vital ingredient: the individual experience that any human writer facing the blank page cannot avoid. Without the raw input of the complicated business that is life, even the most talented AI can only rearrange the books it ingested in its training enough for a few good years in publishing, perhaps, but hardly a sustainable model for literary culture.

Maybe Im thinking too small. Maybe any publisher looking forward to the death of the author would only need to expand the training programme for their writing machines. Perhaps they could hook their AIs up to the daily news, wire them into Spotify, encourage them to make new friends on Twitter and feed it all back into the work. The resulting algorithms would be very different to human beings, of course. But perhaps they would be enough like thinking, feeling beings that their fiction would be communicating something rather marvellous after all.

Richard Lea writes for Guardian books

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If a novel was good, would you care if it was created by artificial intelligence? - The Guardian

Imaging studies are an important part of screening and diagnosis for some cancers, lung, and breast in particular. Such studies have led to more lung and breast cancers being diagnosed at a smaller size compared to what was found prior to the advent of screening programs. One important research question that is currently being explored is whether the use of artificial intelligence to aid in diagnosis can improve the performance of radiologists alone. Lets take a look at what we know so far.

According to the American Cancer Society (ACS), approximately one in eight women will be diagnosed with breast cancer in their lifetime. It is the second leading cause of death from cancer in women.

Breast cancer screening is commonly performed on patients who have no obvious signs of disease. Many of these women are not at high risk for the disease. Nor do they have a family history.

Although many preventive health guidelines recommend screening mammograms, concerns have been raised. For example, one in five abnormal mammograms is a false positive. That means the mammogram was read as positive by a radiologist but proved not to be cancer on biopsy.

Over the span of ten years, about half of women are given a false-positive result. This usually leads to further testing, anxiety, distress, and sometimes unnecessary procedures or treatment.

Experts from Google Health and its subsidiary, Alphabets DeepMind unit, recently worked with Northwestern University, Cancer Research UK Imperial Center, and Royal Surrey County Hospital to examine aspects of radiographic breast cancer diagnosis. In particular, they wanted to better understand the reasons for inaccuracies in the diagnosis of breast cancer. And, they wanted to determine if artificial intelligence could help.

In order to comprehend how AI can be used to improve the results of breast imaging moving forward, it is important to have a basic understanding of how this artificial intelligence system works. This is a type of system known as Deep Learning which involves a three-dimensional model:

The results of this research were recently published in the journal Nature in an article titled International evaluation of an AI system for breast cancer screening. The study compared the results of mammography readings in an artificial intelligence model to those read by radiologists. There were close to 26,000 women from the UK and over 3,000 women in the United States in the study.

The researchers found that the artificial intelligence model reduced both false positives (when patients are told they have cancer when they dont) and false negatives (when the disease is present, but not diagnosed).

Although in this early testing the AI caught cancers missed by radiologists, there were also cases in which it missed cancer that was caught by radiologists. This suggests that AI alone may not be the sole solution moving forward.

With approximately 160,000 deaths in 2018 due to lung cancer, it is the most common cause of cancer death in the United States. The U.S Preventive Services Task Forces (USPSTF) new guidelines for the use of low dose computed tomography has recently been updated for individuals at high risk of having lung cancer.

Lung cancer screening using this type of computed tomography testing has been shown to reduce death by 20-40%. However, similar to breast cancer screening, one ongoing issue with the use of this screening exam has been the high rate of false positives (a result that indicates that a person has a disease when they actually do not). Although low-dose lung CTs have helped immensely in early detection, it has been found that about one-quarter of the suspected nodules are actually not cancerous.

To determine if this could be improved upon, doctors at Northwestern University and Stanford, teamed up with Google to determine if the same type of artificial intelligence, called Deep Learning, could help improve upon our current methods with lung cancer.

Researchers from Google used more than 42,000 CT scans to train this artificial intelligence system to detect cancerous lung nodules on radiology imaging. The study, titled End-to-end lung cancer screening with three-dimensional deep learning on low dose chest computed tomography was published in Natureas well.

Over 6,000 National Lung Cancer Screening Trial cases were tested in this study. In addition, there was an independent evaluation of a set of over a thousand cases. The performance of the artificial intelligence system was compared against radiologists who had evaluated low-dose chest computed tomography scans for patients several of which had confirmation of cancer by biopsy within a year.

This deep-learning artificial intelligence system produced fewer false negatives (a result that indicates that a person does not have a disease when they actually do) as well as fewer false positives. When prior imaging was available, the model performed better than the radiologists (six of them) with an 11% reduction in false positives and a 5% reduction in false negatives.

The Nature study was a retrospective study that examined past cases. This type of study design is not as strong as prospective studies with randomization. Mozziyar Etemadi, MD, Ph.D., one of the authors of the study has said that the next step is to perform a prospective study to see if the tool, when used by a radiologist, can lead to earlier and more accurate diagnosis of cancer.

Another caveat is that it may be some time before AI with deep learning is routinely used in hospital and free-standing radiology suites. The algorithm that is the backbone of the AI-deep learning system is very sophisticated and will undoubtedly require some painstaking work to fully integrate into hospital computer systems. Further, the variability of many cancers could make new scenarios difficult for the deep learning system to interpret if they have not been seen before.

We also need to consider that although AI with deep learning improves some aspects of cancer screening diagnoses, it is not (yet) perfect. It may be that the best way to introduce AI into imaging analysis is to add it to the workflow of radiologists. This is because both have the potential to not catch something or make mistakes.

The performance of the deep learning system shows that there can be a beneficial role of artificial intelligence in cancer screening moving forward. In fact, the use of algorithms that incorporate co-morbidities and risk factors in medicine is not uncommon today. However, the use of such a sophisticated one on its own will most certainly take time. It will also require well-designed prospective studies that follow patients over time. Nonetheless, there is no denying that there will be an important role of artificial intelligence in cancer screening moving forward.

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Originally posted here:
Artificial Intelligence: Can It Improve Results of Cancer Screening... - The Doctor Weighs In

Without the help of AI, the agent has to go through the entire process of marketing, gathering leads, finding the right buyer and following up. Now, the reason for stressing on how time-consuming and tedious this job is, is that the restorative has been found - TruConnectRE.Com.

FALLS CHURCH, Va. (PRWEB) January 27, 2020

Artificial intelligence, as we know it, is going to revolutionize the real estate sector and give it groundbreaking innovation. It is clear that this phenomenal technology is going to impact the future of real estate in a huge way through its ability to gather, analyze and extract information more efficiently, speeding up processes. Other than efficiency, it saves heaps of money in real estate transactions. Experts have foreseen that the advancement of AI in the real estate sector will increase the weight of technical solutions and alter the pertinence of human involvement. In the future, artificial intelligence will, in particular, automate facility management, i.e. the management of the real estate. At this plane, it's not instantly visible how real estate is influenced by artificial intelligence but it is. This impact is on the buying, selling, and maintenance of the Real estate.

With the worldwide market size of $280.6 trillion and nearly $9.6 billion in technology investments, the real estate industry has become one of the most prominent industries in the world. At that rate, one would that this industry would be the most technologically enlightened. However, until very recently, real estate agents would go out into their neighborhoods, knock on doors and introduce themselves to prospective home buyers. Or they would buy lists of contacts and start cold calling. Fortunately for realtors, those days are long gone. The biggest strength of Artificially Intelligent systems lies in having access to tons of data and being able to find patterns in that data, generating insights and inferences while augmenting peoples ability to make decisions based on that data.

Recently, research proved that up to 98% of home buyers now start their research online before they get in touch with a real estate professional and 71% of them settle for the first agent they connect with. With both, the buyer and seller online, one would assume that all that needs to be done now is to find the right buyer and sell to them. Nope. Its not that simple. Without the help of AI, the agent has to go through the entire process of marketing, gathering leads, finding the right buyer and following up. Now, the reason for stressing on how time-consuming and tedious this job is, is that the restorative has been found - TruConnectRE.Com.

TruConnect Real Estate is deeply vested in the agents win. It not only furnishes solid leads for the realtor but also offers a turn-key solution to the problems at hand. With its data-driven, human-powered approach, TruConnectRE.Com provides the realtor with advanced Artificial Intelligence software to manage and track the leads provided. The best part? TruConnectRE charges only a 25% referral fee succeeding in a closing! A powerful lead generation system uses both, inbound and outbound marketing to engage buyers and take them towards conversion. To expedite that, TruConnect also tailors and executes marketing material, establishing an all-in-one platform for the realtor.

With its developing strategy, TruConnect Real Estate does all the work for the realtor by generating thousands of real estate leads from around the world. And it doesnt stop there. They call the turnover, strain and ensure follow-ups until they find the perfect client for an agent to connect with. All the while, they not only increase performance and cut down costs but also rid the agents of the tiresome task of calling leads. This frees up time for the realtor to focus on the buyer, closing 2 to 5 times the deals they normally do. Agents are introduced to screened and effectuate clients and are left to form a relationship and come to an agreement.

Artificial intelligence proves to be very advantageous in streamlining the glacial real estate processes. TruConnectRE.Com with its powerful AI software can now aid the human-led processes to make them accurate and more efficient, helping them serve their client better.

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How Artificial Intelligence (AI) Is Revolutionizing the Real Estate industry - PR Web

Mosquitos cause serious problems throughout the globe, spreading diseases such as malaria, dengue, and zika. According to the World Health Organization (WHO), they are responsible for more than 1 million deaths every year. Now, artificial intelligence (AI) is being used as a way to fight back. This comes at a time when the effects of mosquitoes continues to worsen, especially due to the consequences of climate change.

Back in the summer of 2018, Europe faced a growing threat of the West Nile virus infection. The increased threat was due to the rise of temperatures and wet weather, conditions that are preferred by the insect.

The Institute of Agrifood Research and Technology (IRTA) in Catalonia, Spain is now using artificial intelligence (AI), sensors, and satellite communication in order to automate the process of trapping mosquitoes. The technology also classifies them according to species, sex, age, and if they can cause infection.

IRTA is owned by the government of Catalonia, and they have developed the EU-funded VECTRACK project. Remote sensing and spatial modeling techniques will be adopted, and these will create special maps to help with risk surveillance and assessments.

The project will rely on the Earth Observation Satellite Sentinel service. It will also use ground nodes with optoelectronic sensors in order to remotely count and classify the mosquitos. It is set to be the first transnational and automated vector surveillance system.

VECTRACK was developed by Irideon, a Spanish-German company based in Spain. The company sells sensor-based products to different sectors.

Trapping has been used by countries for a long time in order to control mosquitoes, and manual trap inspections are done by researchers or technicians. These inspections take a lot of time and resources, especially when it comes to classifying mosquitoes accurately by eye.

The test phases with the special traps are being done by the Barcelona Public Health Agency (ASPB). They are responsible for controlling and monitoring mosquitoes in the city.

What makes the traps different from those already in use is the addition of optoelectronic sensors. This enables remote and automated counting and classification of targeted mosquitoes.

According to researcher Dr. Carles Aranda, mosquitoes are attracted to the traps. The traps emit carbon dioxide as an entree and then suck the specimens inwards so that they do not escape.

The sensors collect data for various different parameters including temperature and humidity related to the GPS location of the trap. A digital fingerprint of the mosquitoes is then created with the morphological, physiological, and flight kinetics of the insect.

The VECTRACK sensors are able to be installed throughout most of the globe. They are compatible with many different communication protocols such as 2G, 3G, 4G, Wi-Fi, LPWAN technologies NB-IoT and LoRA, and satellite IoT.

After the sensors collect the data, it is sent to the cloud to be analyzed by algorithms. It is then processed in geographic information systems that are provided by Avia-GIS Software, a Belgian company. The European Space Agency (ESA) provides satellite information which the data is integrated into.

The real-time risk maps that are developed by the researchers through the process can be provided to regional, national, and international public-health bodies like the European Center for Disease Control.

The new technology can be extremely helpful in preventing disease and controlling epidemics.

The Protocol of the Surveillance and Control of mosquito-borne diseases began in 2014. 507 cases of dengue have been detected in Catalonia, highlighting the importance of this technology.

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Artificial Intelligence Used to Fight Against Mosquitoes - Unite.AI