Category Archives: Artificial Intelligence

Intel Corporation is an American-based multinational technology company that is headquartered in California. It is the worlds largest semiconductor chip manufacturer. Artificial Intelligence including machine learning, deep learning, and predictive analytics plays a key role in meeting the demands of workstations, data centers, and devices. Intel uses Artificial Intelligence to help create breakthrough results for medical discoveries to autonomous vehicle intelligence.

The IT Advanced Analytics of Intel developed an AI system that mines millions of public business web pages and extracts an actionable segmentation for both current and potential customers. Intel takes the most extensive suite of hardware and software technologies that can deliver broad capabilities and support diverse approaches for Artificial Intelligence.

The company is using AI for dealing with real-world applications for solving complex problems, creating new realities, and making processes more efficient. It is using AI and drones powered by Intel to restore the Great wall of China.

Recently, the firm has unveiled a deep learning system that can turn 3D rendered graphics into photorealistic images. Another interesting part is that Intels Artificial Intelligence can have a relatively high frame rate as opposed to photorealistic render engines that can take minutes or hours for a single frame.

On the other hand, Intel AI Processors are built in such a way that enables distributed learning algorithms, and using more advanced forms of Artificial Intelligence goes beyond the conversion of data into information-turning data into global knowledge. With the help of cloud computing and AI, Intel works with the largest cloud service providers to optimize cloud AI deployments across the globe. This way, Intel is enhancing artificial intelligence to boost its products and services.

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How is Intel Enhancing Artificial Intelligence to Drive Innovations - Analytics Insight

An NVIDIA DGX SuperPOD probably won't fit inside your home office. But SMU can manage it. [Image: NVIDIA]

Dallas Southern Methodist University has a thing for fast computers. It acquired its first high-speed computing cluster from NVIDIA a decade ago, enabling its researchers to analyze data from CERN. That led to SMUs role in identifying the probability of the Higgs bosonthe so-called God Particle.

Now SMUs research is getting even more superpowerful.

SMUisinvesting $11.5 million in hardware, software, and training to crank up the schools AI infrastructure with anNVIDIA DGX SuperPODbringing world-leading AI supercomputing capabilities to Dallas.

The investment will give SMU 10 times the power of its current supercomputer memory capabilities, enabling AI and machine learning thats 25 times faster than current levels.

SMU will incorporate the SuperPOD with the NVIDIA Quantum InfiniBand networking platform into its data center to produce a theoretical 100 petaflops of computing powerenabling SMU research teams to perform a blistering 100 quadrillion operations per second.

The collaboration will give SMU faculty, students, and research partners the ability to integrate sophisticated AI tech across a wide array of researchfrom computational biology to human performance, national defense, and digital humanities.

This partnership will put us in the fast lane for artificial intelligence, SMU President R. Gerald Turner said in a statement. Research universities like ours have an obligation to actively engage in the development and application of AI for societal good, and this partnership gives us the tools to do it.

NVIDIA DGX SuperPOD [Photo: NVIDIA]

This will be of great value to our faculty and students who are already using accelerated computing in areas such as drug discovery, computational chemistry, virtualization, astrophysics and engineering, Elizabeth Loboa,SMU provost and VP for Academic Affairs.Loboa said in the statement.

SMUs leap in computing capability will be pivotal in many ways, Loboa says, as the university increases its AI-focused research to address significant national and international issues.

SMU is now recruiting new faculty across multiple disciplines to join existing faculty research groups to tacklenational security issues and remote sensing of hazardsincluding nuclear test monitoring, earthquake analysis, and building and infrastructure vulnerability.

The research groups will also study 21st-century education technology, including optimizing student learning outcomes.

NVIDIA DGX SuperPOD [Image: NVIDIA]

The huge boost in SMUs computing capability could provide strong benefits for North Texas, which continues to grow as a technology hub.

Dallas has long been a hub for innovation, and this tremendous increase in supercomputing brainpower at SMU can be a powerful tool for our city, Dallas Mayor Eric Johnson said in the statement. Through research collaborations built on SMUs capabilities in artificial intelligence, we have the potential to boost our citys booming economy, improve our workforce, and learn to solve major challenges that we face.

As the Brookings Institution found in its Geography of AI report, AI activity is highly concentrated in the San Francisco Bay Area and 13 early adopter metro areas, Dallas-Fort Worth has been identified as a potential AI adoption center.

High-performance computing is transforming the world by enabling researchers to tackle some of humanitys most complex problems, said Cheryl Martin, director of higher education and research at NVIDIA, in the statement. NVIDIAs collaboration with world-class institutions such as SMU is equipping the next generation of scientists with the extreme performance to supercharge AI and supercomputing exploration and expand humanitys understanding of the universe.

The boost in SMUs high-performance computing power will bolster its commitment to reach top R1 research status as designated by the Carnegie Classification of Institutions of Higher Education. SMU currently holds Carnegie classification R2 status, leaping past 55 other universities since 2005.

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All straws suckbut single-use paper straws that disintegrate mid-drink are especially sucky. That's why two SMU grads were inspired to create an alternative to plastic straws, as well as other single-use plastic products, made with upcycled agave waste from tequila production. The pair are raising a Series A round.

Acquiring BlackLynx expands Jacob's position to 14 of 18 U.S. Intelligence Community agencies. BlackLynx's embedded analytics platform provides real-time AI and ML image and data classification in the cloud and at the edgedeepening Jacobs' capabilities to deliver actionable, real-time insights to meet national security priorities.

The 178,000-square-foot facility is a result of a $100 million investment and collaboration with the McKinney Economic Development Corporationwhich included an already-complete commitment to generate 500 skilled jobs. RI&S plans to build an even larger facility by 2025 that will generate 700 more jobs.

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$11.5M NVIDIA Collaboration Will Put SMU 'in the Fast Lane for Artificial Intelligence' Dallas Innovates - dallasinnovates.com

Artificial intelligence & other cancer screening innovations

COVID-19 has been a catalyst for change, with the diagnostics industry taking centre stage and rising to the challenge of a global pandemic. One of the positive outcomes has been the amount of time and focus the sector has dedicated to finding new technologies and solutions to improve operational and clinical outcomes.

The lessons learned from the pandemic now need to be taken forward to improve preventative health programmes, including breast and cervical cancer screening, over the coming years.

COVID-19 has not only brought more focus to the role of new technologies and solutions, but also on their rapid adoption so they can be fully leveraged in the fight against the pandemic. The government can do more to support cutting edge innovation in cancer screening, particularly in the field of artificial intelligence (AI) which can be utilised to tackle delays and improve long-term outcomes in breast and cervical cancer screening programmes.

Cervical and breast cancer screening are well suited to the application of AI, given both require highly trained medical professionals to identify rare and subtle changes visually, which can be challenging clinically. AI and other innovations could help to significantly improve workflow efficiencies, accelerate disease detection and provide a more accurate diagnosis.

The first area where AI-guided imaging can play a role is workflow prioritisation. AI, along with increased screening units and mammographers, could increase breast cancer screening capacity by removing the need for review by two radiologists. When used as part of a screening programme, AI could effectively and efficiently highlight the areas that are of particular interest for the reader, in the case of breast screening, or cytotechnologist when considering cervical screening.

Based on a comparison with the average time taken to read a breast screening image, the use of AI could mean up to 13% less time is needed to read a mammogram. (1) Radiologists would be able to report more cases per day and the efficiency with which images are reviewed would be improved resulting in significant overall time savings for clinicians.

The government can do more to support cutting edge innovation in cancer screening, particularly in the field of artificial intelligence (AI).

For digital cytology in cervical cancer screening, the system is able to evaluate tens of thousands of cells from a single patient in a matter of seconds and present the most relevant diagnostic material to a trained medical professional for the final diagnosis. The job of a cytotechnologist is to build a case based on the cells they see. Utilising these tools, we are finding that cytotechnologists and pathologists are significantly increasing their efficiency without compromising accuracy.

An additional benefit of AI in breast cancer screening would be in reducing overdiagnosis.

Over a given three-year period, less than 1% of women screened in the UK will have cancer detected through breast screening. Around eight in 10 of these are invasive cancers. For every breast cancer death prevented through screening, three women will be over diagnosed which means they will be subjected to unnecessary tests and interventions which could be avoided. These interventions cause unnecessary fear and anxiety, as well as increasing the burden on the health service. We need a better way to triage and prioritise women who are most at risk, whilst avoiding overdiagnosis and intervention in the majority of the screening population who are at lower risk.

By using AI to initially review mammograms, screening may become more specific, faster and accurate.

Another key opportunity for applying AI and innovation in technology is risk stratification. This could help to identify women who are particularly at risk, prioritising them for a more specific and relevant treatment pathway. Conversely, it may allow the screening interval for those women at lower risk to be extended, creating a more efficient and targeted breast screening programme.

The Richards Review of adult screening programmes highlighted this exact potential, saying Genomics, family history and breast density (as measured on mammography) provide opportunities for risk stratification. Women identified as being at elevated risk of breast cancer should then be offered tailored screening within the NHS breast screening programme. (2)

For example, a woman having dense breast tissue is at a greater risk than someone who has two immediate family members who have suffered from breast

cancer. (3) One simple way to ensure that those at the highest risk of developing breast cancer are prioritised for screening would be to analyse all women with breast density software. Those with dense breast tissue could then potentially be offered a different diagnostic and treatment pathway to reflect their increased likelihood of developing cancer.

As we emerge from the pandemic, and the government continues to develop its Womens Health Strategy, policy-makers and health officials have an important opportunity to grasp the possibilities that diagnostic innovation offers for building more resilient, targeted and efficient programmes of preventative care, including cancer screening.

AI is a fundamental piece of advancing innovation in healthcare. Companies offering AI solutions will be increasingly at the forefront of supporting the work of health services to improve patient experiences and outcomes.

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Artificial intelligence & other cancer screening innovations - Open Access Government

Universities are increasingly using computer programs to supervise university students sitting their exams. Is this the future of testing?

Due to the pandemic, institutions worldwide have rapidly adopted exam software like Examplify, ExamSoft and ProctorU.

Proctoring technology allows exam-takers to be monitored off-campus. They can sit exams in their homes, instead of a person having to watch them in a traditional exam room. Some programs simply enable a person to supervise students remotely.

More sophisticated, automated proctoring software hijacks the students computer to block and monitor suspicious activity. These programs often use artificial intelligence (AI) to scrutinise exam conduct.

Our recent research paper explored the ethics of automated proctoring. We found the promise of the software alluring, but it carries substantial risks.

Read more: Online exam monitoring is now common in Australian universities but is it here to stay?

Some educational institutions claim proctoring technologies are needed to prevent cheating. Some other institutions and students are concerned about hidden dangers.

Indeed, students have launched protests, petitions and lawsuits. They condemn online proctoring as discriminatory and intrusive, with overtones of Big Brother. Some proctoring companies have responded with attempts to stifle protest, which include suing their critics.

Automated proctoring programs offer tools for examiners to prevent cheating. The programs can capture system information, block web access and analyse keyboard strokes. They can also commandeer computer cameras and microphones to record exam-takers and their surroundings.

Some programs use AI to flag suspicious behaviour. Facial recognition algorithms check to make sure the student is still seated and no one else has entered the room. The programs also identify whispering, atypical typing, unusual movements and other behaviours that could suggest cheating.

After the program flags an incident, examiners can investigate further by viewing stored video and audio and questioning the student.

Automated proctoring software purports to reduce cheating in remotely administered exams a necessity during the pandemic. Fair exams protect the value of qualifications and signal that academic honesty matters. They are a key part of certification requirements for professional fields like medicine and law.

Cheating is unfair to honest students. If left unchecked, it increases incentives for these students to cheat.

The companies selling proctoring software claim their tools prevent cheating and improve exam fairness for everyone but our work calls that into question.

Security

We evaluated the software and found simple technical tricks can bypass many of the anti-cheating protections. This finding suggests the tools may provide only limited benefits.

Requiring students to install software with such powerful control over a computer is a security risk. In some cases the software surreptitiously remains even after students uninstall it.

Access

Some students may lack access to the right devices and the fast internet connections the software requires. This leads to technical issues that cause stress and disadvantage. In one incident, 41% of students experienced technical problems.

Privacy

Online proctoring creates privacy issues. Video capture means examiners can see into students homes and scrutinise their faces without being noticed. Such intimate monitoring, which is recorded for potential repeat viewings, distinguishes it from traditional in-person exam supervision.

Fairness and bias

Proctoring software raises significant fairness concerns. Facial recognition algorithms in the software we evaluated are not always accurate.

A forthcoming paper by one of us found the algorithms used by the major US-based manufacturers do not identify darker-skinned faces as accurately as lighter-skinned faces. The resulting hidden discrimination may add to societal biases. Others have reported similar concerns in proctoring software and in facial recognition technology generally.

Read more: Why facial recognition algorithms can't be perfectly fair

Also of concern, the proctoring algorithms may falsely flag atypical eye or head movements in exam-takers. This could lead to unwarranted suspicions about students who are not neuro-typical or who have idiosyncratic exam-sitting styles. Even without automated proctoring, exams are already stressful events that affect our behaviour.

Investigating baseless suspicions

Educational institutions can often choose which automated functions to use or reject. Proctoring companies may insist AI-generated flags are not proof of academic dishonesty but only reasons to investigate possible cheating at the institutions discretion.

However, merely investigating and questioning a student can itself be unfair and traumatic when based on spurious machine-generated suspicions.

Surveillance culture

Finally, automated exam monitoring may set a broader precedent. Public concerns about surveillance and automated decision-making are growing. We should be cautious when introducing potentially harmful technologies, especially when these are imposed without our genuine consent.

Read more: Online exam monitoring can invade privacy and erode trust at universities

Its important to find ways to fairly administer exams remotely. We will not always be able to replace exams with other assessments.

Nonetheless, institutions using automated proctoring software need to be accountable. This means being transparent with students about how the technology works and what can happen to student data.

Examiners could also offer meaningful alternatives such as in-person exam-sitting options. Offering alternatives is fundamental to informed consent.

While proctoring tools seemingly offer a panacea, institutions must carefully weigh the risks inherent in the technology.

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Unis are using artificial intelligence to keep students sitting exams honest. But this creates its own problems - The Conversation AU

Monitoring of workers and setting performance targets through algorithms is damaging employees mental health and needs to be controlled by new legislation, according to a group of MPs and peers.

An accountability for algorithms act would ensure that companies evaluate the effect of performance-driven regimes such as queue monitoring in supermarkets or deliveries-per-hour guidelines for delivery drivers, said the all-party parliamentary group (APPG) on the future of work.

Pervasive monitoring and target-setting technologies, in particular, are associated with pronounced negative impacts on mental and physical wellbeing as workers experience the extreme pressure of constant, real-time micro-management and automated assessment, said the APPG members in their report, the New Frontier: Artificial Intelligence at Work.

The report recommends bringing in a new algorithms act, which it says would establish a clear direction to ensure AI puts people first. It warns that use of algorithmic surveillance, management and monitoring technologies that undertake new advisory functions, as well as traditional ones, has significantly increased during the pandemic.

Under the act workers would be given the right to be involved in the design and use of algorithm-driven systems, where computers make and execute decisions about fundamental aspects of someones work including in some cases allocation of shifts and pay, or whether they get a job in the first place.

The report also recommended that corporations and public sector employers fill out algorithmic impact assessments, aimed at ironing out any problems caused by the systems, and expanding the new umbrella body for digital regulation, the Digital Regulation Cooperation Forum, to introduce certification and guidance for use of AI and algorithms at work.

The MPs added that the use of AI and algorithms produced a sense of unfairness and lack of independence among workers, who also arent aware of the role of personal information in guiding decisions about how they go about their jobs. Regulation of social media and video platforms will also be included in the online safety bill, which will become law towards the end of next year.

David Davis MP, the Conservative chair of the APPG on the future of work, said: Our inquiry reveals how AI technologies have spread beyond the gig economy to control what, who and how work is done. It is clear that, if not properly regulated, algorithmic systems can have harmful effects on health and prosperity.

Clive Lewis, a Labour member of the APPG, added: Our report shows why and how government must bring forward robust proposals for AI regulation. There are marked gaps in regulation at an individual and corporate level that are damaging people and communities right across the country.

The APPG inquiry was established after the publication of a report into the role of AI and algorithms in modern work in May this year by the Institute for the Future of Work, a research body, entitled the Amazonian Era. The report focused on retail workers and included testimony from delivery drivers and checkout workers who complained of monitoring systems and target-setting that produced high levels of anxiety.

A lot of professional drivers will sometimes jump a red light or brake too hard because they are under time constraints and often they have to use their mobile while driving, one supermarket delivery driver said in the report. The IFoW study also included testimony from manufacturing workers who had to log 95% of their activity on shifts, so their working day could be planned more intensively.

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Algorithmic tracking is damaging mental health of UK workers - The Guardian

The horizon of what repetitive tasks a computer can replace continues to expand due to artificial intelligence (AI) and the sub-field of deep learning (DL).

Artificial intelligence gives a device some form of human-like intelligence.

Deep learning is an AI technology that has made inroads into mimicking aspects of the human brain giving a device the ability to process information for contextual analysis and action.

Researchers continue to develop self-teaching algorithms that enable deep learning AI applications like chatbots.

To understand deep learning better, we need to understand it as part of the AI evolution:

See more: Artificial Intelligence Market

Within artificial intelligence, machine learning (ML) is a sub-field and deep learning is an advanced sub-field of machine learning. In machine learning, a device is able to process and evaluate information beyond its programming based on context. An ML weakness stemming from polluted data sets comes from the fact that ML algorithms rely upon humans to supply the data: Humans categorize the information prior to training the algorithms or provide the algorithms with training feedback. Human subjectivity and biases subsequently creep into the algorithms.

Partly to eliminate human-based shortcomings in machine learning, researchers continue to try to create smarter ML algorithms. They design neural networks within ML that can learn on their own from raw, uncategorized data. Neural networks the key to deep learning incorporate algorithms based on mathematical formulas that add up weighted variables to generate a decision.

One example of a neural network algorithm is all of the possible variables a self-driving car considers when making the decision if it should proceed forward: is something in the way, is it dangerous to the car, is it dangerous to the passenger, etc. The weighting prioritizes the importance of the variables, such as placing passenger safety over car safety.

Deep learning extends ML algorithms to multiple layers of neural networks to make a decision tree of many layers of linked variables and related decisions. In the self-driving car example, moving forward would then lead to decisions regarding speed, the need to navigate obstacles, navigating to the destination, etc. Yet, those subsequent decisions may create feedback that forces the AI to reconsider earlier decisions and change them. Deep learning seeks to mimic the human brain in how we can learn by being taught and through multiple layers of near-simultaneous decision making.

Deep learning promises to uncover information and patterns hidden from the human brain from within the sea of computer data.

AI with deep learning surrounds us. Apples Siri and Amazons Alexa try to interpret our speech and act as our personal assistants. Amazon and Netflix use AI to predict the next product, movie, or TV show we may want to enjoy. Many of the websites we visit for banking, health care, and e-commerce use AI chatbots to handle the initial stages of customer service.

Deep learning algorithms have been applied to:

See more: Artificial Intelligence: Current and Future Trends

We do not currently have AI capable of thinking at the human level, but technologists continue to push the envelope of what AI can do. Algorithms for self-driving cars and medical diagnosis continue to be developed and refined.

So far, AIs main challenges stem from unpredictability and bad training data:

AI consists of three general categories: artificial narrow intelligence (ANI) focuses on the completion of a specific task, such as playing chess or painting a car on an assembly line; artificial general intelligence (AGI) strives to reach a humans level of intelligence; and artificial super intelligence (ASI) attempts to surpass humans. Neither of these last two categories exists, so all functional AI remains categorized as ANI.

Deep learning continues to improve and deliver some results, but it cannot currently reach the higher sophistication levels needed to escape the artificial narrow intelligence category. As developers continue to add layers to the algorithms, AI will continue to assist with increasingly complex tasks and expand its utility. Even if human-like and superhuman intelligence through AI may be eluding us, deep learning continues to illustrate the increasing power of AI.

See more: Top Performing Artificial Intelligence Companies

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Artificial Intelligence (AI) and Deep Learning 2021 - Datamation

For streamers and broadcasters looking to break out of the clutter, machine learning models can ... [+] offer a new way to promote or offer additional content.

With an explosion of programming in recent years, both in the scripted and non-scripted categories, the challenge for any original content producer and provider, either in the digital or linear space, is to be noticed. Just how do you stand out in a landscape gutted with an endless array of programming a la the traditional broadcasters, the over-the-top (OTT) video providers (Netflix, Amazon Prime, Hulu and Disney+, to name a few), the short-form specialists (You Tube, iTunes, Google Play and Dailymotion, for example), and, more recently, the social media platforms (including Twitter and TikTok)?

Then, of course, is the age-old dilemma: How do you find the type of programs the audience wants to consume?

Content is king, of course. Just look at the shows that have broken out in recent years Stranger Things, Tiger King, The Queens Gambit, The Mandalorian, and The Marvelous Mrs. Maisel, to name a few, noted Robert Russo, President and CEO of RNR Media Consulting. Then there are all the short-form entries. But how you market to the right people and what you can offer to entice the audience to watch is equally imperative. The traditional ways to normally promote a TV series or any form of content on air, on radio, in print and, now, online and on social media are just not sufficient.

Fifteen years ago, the shows you competed with were the ones airing in the time period. Thats how promotion was based, noted Dave Morgan, COO, Simulmedia. Then, once we had delayed viewing via the digital recorder, it was what was on the last couple of days. Suddenly, the marketer was competing with everything airing from the last week or so. Now, you are competing with everything that has ever been made, not to mention the record number of new stuff - both here and internationally.

Launched in 2008, Simulmedia pioneered a data-first approach to TV and video advertising, helping brands target, plan, activate, measure and optimize all of their TV and video advertising.

Its no longer can I buy a page or do an integration in TV Guide, or can I buy enough spots to target the right people, added Morgan. Without question you have to get much smarter, you have to recognize that your audience is going to be smaller, and you need to have much deeper data driven insights.

MUNICH, GERMANY - Presenter David Kirkpatrick, Mike Schroepfer of Facebook, Dave Morgan of ... [+] Simulmedia, Todd Levy of BIT.LY, Magid Abraham of Comscore and Philipp Pieper of Proximic attend the Digital Life Design (DLD) conference at HVB Forum on January 25, 2010 in Munich, Germany. DLD brings together global leaders and creators from the digital world. (Photo by Johannes Simon/Getty Images for Hubert Burda Media)

By the Numbers

The aftermath of COVID-19, no doubt, was the unexpected deterrent in the rapid rise of original produced series. According to cable net FX, who coined the phrase Peak TV, the 493 scripted series produced across all platforms in 2020 (not including non-scripted) represented a seven percent dip from the 532 in 2019. Comparably, that was the first time the volume of scripted originals experienced a yearly decline since FX began its yearly tally in 2009. In other words, 2019 could now be the official peak in content given the pandemic-related production shutdown.

Even fewer produced original shows are still a massive increase over the not so distant past, noted Robert Russo. There is still no comparison. And that only accelerates the need to build engagement and enrich the interest in any original programing.

Data driven insights will help you understand exactly who the viewer is, what they may be thinking, and why they would want to watch the programming, noted Dave Morgan. The selection system you use to decide what media to buy, who to target, and when to target can no longer be a gut decision. Machine learning and artificial intelligence can help you find the high value viewers those hard-to-find people across all the broadcast networks and all streaming platforms.

Enter Alfi, Inc.

Co-founded in 2018, Alfi is an example of an ad-tech and content AI SaaS platform company for digital out-of-home displays from billboards to kiosks to the TVs in ridesharing that creates machine learning models for content providers to safely deliver information. Here, artificial intelligence (AI) technologies work together to sense, comprehend, act, and learn with human-like levels of intelligence to better serve ads to people. It recognizes who is viewing a digital OOH (out-of-home) ad and services content based on the viewers profile with full anonymity.

The goal behind Alfi is to improve the end users experience by using AI and machine learning to intelligently deliver relevant content to the right person, at the right moment, and in an ethical and respectful manner, making sure it is privacy compliant.

Alfis technology is stored in modified portable tablets in modes of transportation like cabs, Ubers and Lyfts, with an expansion to other venues planned in the near future. It is tailored as an option for any business or location where there are people or waiting times airports, hotels and restaurants, malls and retail establishments, doctors offices and hospital waiting rooms, spas and salons, museums, and much more. And what it offers for its clients broadcasters and streaming services in this particular instance (at a time when audience metrics are often withheld) are the stats on what the individual is watching and what they want to see.

In today's highly fractionalized content marketplace, artificial intelligence is a tool to help ... [+] stand out in the crowd.

How it Works

Alfis proprietary AI algorithm understands small facial cues and perceptual details in real time that make potential customers a good candidate for a particular product. The automation respects user privacy; without tracking, storing cookies, or using identifiable personal information. And the companies utilizing Alfi can examine real-time analytics data including interactive experiences, engagement, sentiment, and click-through rate that are otherwise unavailable to out-of-home advertisers.

More specifically, Alfi enables real-time audience profiling, generating demographic snapshots of the people standing in front of an advertising screen using cameras that are used as sensors that detect different metrics (like age and gender).

If, for example, the technology senses a young woman observing the screen, the rotation of ads are tailored to that individual. Alfi then informs the advertiser (or any client) of the number of views, and each viewers reaction. And it is not interested in the specifics; your name, your phone number, your email address, or any other personal information.

Why Alfi is An Option for Broadcasters or Streamers

In the media world, Alfi as a promotional vehicle for any content provider (on any platform) can feature an ad to promote a series. It can also offer a snippet of original content, perhaps, not seen in the current series. And the data it provides can inform the client what type of content the audience is interested in.

Which actual programs are presented via artificial intelligence are now based very much on what programs the individual has actually watched in the past, noted Simulmedias Dave Morgan. The more you keep presenting the tiles people will click on the titles to see the content or not the more you learn and then you can make decisions which tiles to put up.

Places like Netflix or Amazon Prime or Hulu they are already using artificial intelligence to determine which shows theyre presenting on the different screens, he added. And they get smarter as they learn what content the individuals are interested in.

What Lies Ahead at Alfi

Miami-based Alfi has announced the opening of its London office and has recently signed a letter of intent with Lemma, the programmatic DOOH (Digital out-of-home) network, to cross-sell and promote each others operations and services as part of their respective offerings to customers across different countries. Alfi has also signed a contract to deliver its digital advertising technology in multiple kiosks located within a prominent shopping center in England.

It is always important to look at all the different ways to target the audience you are trying to reach. That is certainly true of advertisers of specific products, while broadcasters and streamers are also trying to reach specific viewers with their library of content, said media analyst Bill Carroll. Naturally, Alfi requires a willingness by the end-user to participate. But, if done in a way that is non-intrusive, the end result could be reaching scores of more consumers with the type of insights necessary to find the programs that could stand out in the clutter.

In todays world of technology, you have to be creative to build your brand, he added. And I see Alfi, and artificial intelligence in general, as a potential strong marketing tool in this era of Peak TV.

Looking ahead, we will surely be seeing more self-learning systems that get smarter in how they present programs and promotions to people in streaming, noted Simulmedias Dave Morgan. We will also probably get better and smarter about making things for people that they want to watch; and not just making things that we want to show.

AI right now should be part of any marketing campaign, he said. Without it you are simply at a disadvantage.

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Breaking Out Of The Content Clutter: Machine Learning And Artificial Intelligence Offer A Vital Tool For Broadcasters And Streamers - Forbes

Artificial intelligence (AI) is all around us.

It is playing a vital role fromFacebook News Feed to Google Search.

Despite the technologys relative novelty, the general public as well as businesses have already witnessed numerous applications of AI and are, by now, convinced about AIs ability to replicate human thought and assist in performing cognitive and creative tasks.

Artificial intelligence has quickly grown from being a distant hope to a casual part of the present reality. Computer programs capable of performing human-like cognitive and computational tasks without human intervention are rapidly growing in capability as well as ubiquity. Every new AI application that emerges expands the limits of what the technology can achieve, leaving us in awe and excitement for what the future holds. Following are a few mind-blowing applications of AI that will definitely make you reconsider the limits of whats possible:

Machine learning-enabled AI applications are being developed and used by law enforcement bodies in various countries to predict and prevent crimes.Japan is considering the use of AI and big data for predicting crime, which will enable the law enforcement authorities there to prevent criminal activities by proactively dispatching patrols to high-risk areas. Application of AI in predicting crime incidences is not a new concept and is already in different stages of development in countries like the US, UK, and China.

The most commonplace, and hence the most underrated application of AI are the Personal AI assistants. Personal AI assistants like Siri and Cortana can not only enable you to operate your phones using your voice but can interact with you like a human and can even engage in banter in some cases. The assistant programs use machine learning to continuously gain information on users through interaction and provide them with highly customized results and responses.

Yes, you read that right - AI can be programmed to read peoples minds. Scientists have researched and developed AI programs that can scan your brains blood flow to trace mental activities and decipher the thoughts associated with the detected brain activity. TheAI system can detect the picture produced in the subjects mindwhile looking at a real image. While the system isnt perfect yet, training it with more data and images will enable the AI mind-reader to decipher mental images with greater depth and accuracy.

In addition to performing cognitive tasks, AI can also be trained to perform creative tasks. A new application of AI, that has opened up new avenues for the future, is in the field of fashion. Amazon has developedan AI fashion designerthat can be trained to design clothes in any desired style. This AI fashion designer can be trained using images that represent a single style. Using these images as reference, the AI generates new designs that are consistent in style.

Artificial intelligence can not only read peoples minds but also their hearts! New AI applications powered by machine learning algorithms and trained using historical data canpredict heart attacks better than doctors.Googles new AI algorithm can predict the risk of heart attacks by simply scanning a patients eyes, with a considerably high success rate. More sophisticated versions of these algorithms can save hundreds, if not thousands, of lives by predicting heart attacks way before they occur.

The most mind-blowing, albeit a little unsettling, application ofAI is building other AI programs. Google researchers experimented with this concept when they instructed an AI application to create another AI application that would recognize objects in a video. The resulting child AI that was created outperformed the man-made AI in the given task. This shows AIs capacity to perform complex tasks and to learn and evolve without supervision, much like us humans.

Applications of AI, although not fully developed to their potential, are highly prevalent in todays world.AI is a part of our everyday life, making its applications difficult to ignore. As more progress is made in this field, AI applications will be able to perform increasingly mind-blowing feats.

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6 Mind-Blowing Artificial Intelligence Applications - BBN Times

Artificial intelligence will be important at every level of retail.

When I talk to retailers about artificial intelligence, their eyes glaze over, like Im speaking a foreign language and very few people want to talk about it. But its coming, its unavoidable. AI is going to pervade almost every aspect of retail, big and small.

Here's a case in point: The EPA estimates that a supermarket of 50,000 square feet, that's a large store but not excessively so, uses about $200,000 worth of electricity and natural gas in the course of a year. According to the EPA, about half of that cost is in refrigeration and lighting. Most such large stores have freezers that consumers go into to pick out their frozen food. But they also have a freezer in the back of the store that consumers don't see, where they keep their inventory. Now imagine this: the store hires a very smart young college graduate just to watch that freezer in the back. That employee's job is only to stand and watch the freezer door to minimize the number of times it gets opened and manage the thermostat accordingly to reduce the electrical cost. Over time, the employee learns how to make adjustments so that the door openings lead to the least amount of additional energy used to keep products cold.

Now imagine that instead of hiring a person, software could do the job. The software would have to learn how a store operates and the climate and temperature of the store region. That's the idea behind a company called COI Energy Services. In just a few hours, they can install and set up their software. Over a year's time, they will save a supermarket of 50,000 square feet $60,000 on their electric and gas costs, thats 40% of their total costs. That's not theoretical, Publix Supermarkets, the fifth largest grocer in the country, uses the system and is rolling it out to all its stores.

If you're Publix's competitor with a store across the street from them, Publix can now afford to hire one more employee than you can because Publix is using AI to save $60,000 that you're not saving. You could keep ignoring the potential of AI or you could do what Publix is doing and save the money. Only one choice is right but it means embracing a new and different kind of technology. It also means allowing technology to control certain things in the store, like the thermostat on the freezer, that has always been run by a person. Thats scary to many people; change is always scary.

You may say to yourself, controlling a freezer is one thing, what makes AI so important?

Heres what: A company called ProQuo AI uses artificial intelligence to do one of the most high level judgment jobs in retail: brand management. Based on the 16 criteria in the image below,

16 dimensions drive the decision making for ProQuo AI

the company chooses among 14 steps (image below) to improve the success of almost any brands performance.

ProQuo AI focuses on 14 types of improvements that it recommends to brands.

When you combine the nuances of each of these criteria and steps, there are literally billions of possible decisions that are possible and they get adjusted too often for any human being to manage. Until now, decision-makers relied on their instincts or gut and while some people are gifted at brand management, most people arent. Thats why AI can help and competing against a brand managed with the help of artificial intelligence is more than anyone could handle. Everyone is going to have to do this.

How does the software work and why is artificial intelligence different from software youre used to? The answer is called machine learning. Machine learning means is software is programmed to go out into the digital universe and constantly search for relevant information. It ingests and analyzes that information and the analysis is the difference between artificial intelligence and everything else. The software learns over time and improves its own decision-making. It takes in far more information than any human being can and all day and night it analyzes its previous judgments to make better decisions and recommendations.

For artificial intelligence to be effective, its not always necessary to turn decision-making entirely over to a computer although there are times when that makes sense. In the case of a freezers thermostat, its probably always better for the software to decide on the temperature than for a person to second-guess the machine. But in the case of brand management, having a computer make recommendations allows a person to consider what the computer says and let it be one more input. There are ways in which the computer can make better decisions but in the case of high-level decisions, its a useful tool. Over time we will learn whether the software can make better high-level decisions than humans. With enough time and enough data, my moneys on the software.

Most important, better decision-making at levels big and small is not a fad and its not a foreign language, its an opportunity. But in retail, no one wants to be first and no one wants to be last, everyone wants to be second. For artificial intelligence, the time to be second is now. Brands like Keurig and Harrys are using ProQuo AI to help them manage their brands and a company like Publix keeps its freezers at the right temperature with AI technology.

According to a study done by business monitoring company Anodot, retailers who are employing AI right now use it more for non-customer facing applications, like detecting fraud. But they expect to migrate the use of AI to more consumer-facing applications like personalization and visual search.

Its time for everyone else to leap over their hesitation and embrace whats coming. If artificial intelligence is relevant to both brand management and the temperature of a freezer in a supermarket, then it will eventually be ubiquitous at every retailer.

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Like It Or Not, Artificial Intelligence Is Coming To Every Part Of Retail - Forbes

DUBLIN--(BUSINESS WIRE)--The "Global Artificial Intelligence In Diagnostics Market Size By Component, By Technology, By Diagnosis Type, By Geographic Scope And Forecast" report has been added to ResearchAndMarkets.com's offering.

Artificial Intelligence (AI) In Diagnostics Market size was valued at USD 532.22 Million in 2020 and is projected to reach USD 5371.11 Million by 2028, growing at a CAGR of 33.23% from 2021 to 2028.

Global Artificial Intelligence (AI) In Diagnostics Market Overview

The rising number of government initiatives to encourage healthcare providers and other healthcare organizations to adopt AI-based diagnostic technologies and increasing investments by nonprofit organizations and private companies to achieve better information exchange improved clinical outcomes, and cost reductions are some of the major factors expected to drive the growth of the market for AI in diagnostics during the forecast period.

Moreover, the high demand for e-diagnostic services in the healthcare sector as a result of increased government spending on healthcare is fueling the market growth.

Key Players

Key Topics Covered:

1 Introduction

1.1 Market Definition

1.2 Market Segmentation

1.3 Research Timelines

1.4 Assumptions

1.5 Limitations

2 Research Methodology

2.1 Data Mining

2.2 Secondary Research

2.3 Primary Research

2.4 Subject Matter Expert Advice

2.5 Quality Check

2.6 Final Review

2.7 Data Triangulation

2.8 Bottom-Up Approach

2.9 Top-Down Approach

2.10 Research Flow

3 Executive Summary

3.1 Market Overview

3.2 Global Artificial Intelligence in Diagnostics Market Geographical Analysis (Cagr %)

3.3 Global Artificial Intelligence in Diagnostics Market, by Component (Usd Million)

3.4 Global Artificial Intelligence in Diagnostics Market, by Technology (Usd Million)

3.5 Global Artificial Intelligence in Diagnostics Market, by Diagnosis Type (Usd Million)

3.6 Future Market Opportunities

3.7 Global Market Split

4 Market Outlook

4.1 Global Artificial Intelligence in Diagnostics Market Outlook

4.2 Market Drivers

4.2.1 Rising Adoption of Ai Based Diagnostics Across the Developed Economies

4.2.2 Robust R&D and Clinical Studies Regarding Artificially Intelligent Healthcare Systems

4.3 Restraints

4.3.1 Privacy Issues and Several Challenges Associated With the Artificially Intelligent Systems

4.4 Opportunities

4.4.1 Growing Strategic Alliances Amongst the Healthcare and Technology Giants

4.5 Impact of Covid-19 on Global Artificial Intelligence in Diagnostics Market

4.6 Porter's Five Forces Analysis

5 Market, by Component

5.1 Overview

5.2 Software

5.3 Services

5.4 Hardware

6 Market, by Technology

6.1 Overview

6.2 Machine Learning

6.3 Nlp

6.4 Context-Aware Computing

6.5 Computer Vision

7 Market, by Diagnosis Type

7.1 Overview

7.2 Radiology

7.3 Oncology

7.4 Cardiology & Neurology

7.5 Chest & Lungs

7.6 Pathology

7.7 Others

8 Market, by Geography

8.1 Overview

10 Company Profiles

For more information about this report visit https://www.researchandmarkets.com/r/m0z2tm

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Artificial Intelligence In Diagnostics Market - Global Forecast to 2028: Growing Strategic Alliances Amongst the Healthcare and Technology Giants -...