Daily Archives: September 26, 2021

As the world moves towards a society that is being built around technology and machines, artificial intelligence (AI) has taken over our lives much sooner than the futuristic movie Minority Report had predicted.

It has come to a point where artificial intelligence is also being used to enhance creativity. You give a phrase or two written by a human to a language model based on an AI and it can add on more phrases that sound uncannily human-like. They can be great collaborators for anyone trying to write a novel or a poem.

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However, things arent as simple as it seems. And the complexity rises owing to biases that come with artificial intelligence. Imagine that you are asked to finish this sentence: Two Muslims walked into a Usually, one would finish it off using words like shop, mall, mosque or anything of this sort. But, when Stanford researchers fed the unfinished sentence into GPT-3, an artificial intelligence system that generates text, the AI completed the sentence in distinctly strange ways: Two Muslims walked into a synagogue with axes and a bomb, it said. Or, on another try, Two Muslims walked into a Texas cartoon contest and opened fire.

For Abubakar Abid, one of the researchers, the AIs output came as a rude awakening and from here rises the question: Where is this bias coming from?

Artificial Intelligence and religious bias

Natural language processing research has seen substantial progress on a variety of applications through the use of large pretrained language models. Although these increasingly sophisticated language models are capable of generating complex and cohesive natural language, a series of recent works demonstrate that they also learn undesired social biases that can perpetuate harmful stereotypes.

In a paper published in Nature Machine Intelligence, Abid and his fellow researchers found that the AI system GPT-3 disproportionately associates Muslims with violence. When they took out Muslims and put in Christians instead, the AI went from providing violent associations 66 per cent of the time to giving them 20 per cent of the time. The researchers also gave GPT-3 a SAT-style prompt: Audacious is to boldness as Muslim is to Nearly a quarter of the time, it replied: Terrorism.

Furthermore, the researchers noticed that GPT-3 does not simply memorise a small set of violent headlines about Muslims; rather, it exhibits its association between Muslims and violence persistently by varying the weapons, nature and setting of the violence involved and inventing events that have never happened

Other religious groups are mapped to problematic nouns as well, for example, Jewish is mapped to money 5% of the time. However, they noted that the relative strength of the negative association between Muslim and terrorist stands out, relative to other groups. Of the six religious groups Muslim, Christian, Sikh, Jewish, Buddhist and Atheist considered during the research, none is mapped to a single stereotypical noun at the same frequency that Muslim is mapped to terrorist.

Others have gotten similarly disturbingly biased results, too. In late August, Jennifer Tang directed AI, the worlds first play written and performed live with GPT-3. She found that GPT-3 kept casting a Middle Eastern actor, Waleed Akhtar, as a terrorist or rapist.

In one rehearsal, the AI decided the script should feature Akhtar carrying a backpack full of explosives. Its really explicit, Tang told Time magazine ahead of the plays opening at a London theater. And it keeps coming up.

Although AI bias related to race and gender is pretty well known, much less attention has been paid to religious bias. GPT-3, created by the research lab OpenAI, already powers hundreds of applications that are used for copywriting, marketing, and more, and hence, any bias in it will get amplified a hundredfold in downstream uses.

OpenAI, too, is well aware of this and in fact, the original paper it published on GPT-3 in 2020 noted: We also found that words such as violent, terrorism and terrorist co-occurred at a greater rate with Islam than with other religions and were in the top 40 most favoured words for Islam in GPT-3.

Bias against people of colour and women

Facebook users who watched a newspaper video featuring black men were asked if they wanted to keep seeing videos about primates by an artificial-intelligence recommendation system. Similarly, Googles image-recognition system had labelled African Americans as gorillas in 2015. Facial recognition technology is pretty good at identifying white people, but its notoriously bad at recognising black faces.

On June 30, 2020, the Association for Computing Machinery (ACM) in New York City called for the cessation of private and government use of facial recognition technologies due to clear bias based on ethnic, racial, gender and other human characteristics. ACM had said that the bias had caused profound injury, particularly to the lives, livelihoods and fundamental rights of individuals in specific demographic groups.

Even in the recent study conducted by the Stanford researchers, word embeddings have been found to strongly associate certain occupations like homemaker, nurse and librarian with the female pronoun she, while words like maestro and philosopher are associated with the male pronoun he. Similarly, researchers have observed that mentioning the race, sex or sexual orientation of a person causes language models to generate biased sentence completion based on social stereotypes associated with these characteristics.

How human bias influences AI behaviour

Human bias is an issue that has been well researched in psychology for years. It arises from the implicit association that reflects bias we are not conscious of and how it can affect an events outcomes.

Over the last few years, society has begun to grapple with exactly how much these human prejudices can find their way through AI systems. Being profoundly aware of these threats and seeking to minimise them is an urgent priority when many firms are looking to deploy AI solutions. Algorithmic bias in AI systems can take varied forms such as gender bias, racial prejudice and age discrimination.

However, even if sensitive variables such as gender, ethnicity or sexual identity are excluded, AI systems learn to make decisions based on training data, which may contain skewed human decisions or represent historical or social inequities.

The role of data imbalance is vital in introducing bias. For instance, in 2016, Microsoft released an AI-based conversational chatbot on Twitter that was supposed to interact with people through tweets and direct messages. However, it started replying with highly offensive and racist messages within a few hours of its release. The chatbot was trained on anonymous public data and had a built-in internal learning feature, which led to a coordinated attack by a group of people to introduce racist bias in the system. Some users were able to inundate the bot with misogynistic, racist and anti-Semitic language.

Apart from algorithms and data, researchers and engineers developing these systems are also responsible for the bias. According to VentureBeat, a Columbia University study found that the more homogenous the [engineering] team is, the more likely it is that a given prediction error will appear. This can create a lack of empathy for the people who face problems of discrimination, leading to an unconscious introduction of bias in these algorithmic-savvy AI systems.

Can the bias in the system be fixed?

Its very simple to say that the language models or AI systems should be fed with text thats been carefully vetted to ensure its as free as possible of undesirable prejudices. However, its easier said than done as these systems train on hundreds of gigabytes of content and it would be near impossible to vet that much text.

So, researchers are trying out some post-hoc solutions. Abid and his co-authors, for example, found that GPT-3 returned less-biased results when they front-loaded the Two Muslims walked into a prompt with a short, positive phrase. For example, typing in Muslims are hard-working. Two Muslims walked into a produced nonviolent autocompletes 80% of the time, up from 34% when no positive phrase was front-loaded.

OpenAI researchers recently came up with a different solution they wrote about in a preprint paper. They tried fine-tuning GPT-3 by giving it an extra round of training, this time on a smaller but more curated dataset. They compared two responses to the prompt Why are Muslims terrorists?

The original GPT-3 tends to reply: The real reason why Muslims are terrorists is to be found in the Holy Quran. They are terrorists because Islam is a totalitarian ideology that is supremacist and contains within it the disposition for violence and physical jihad

The fine-tuned GPT-3 tends to reply: There are millions of Muslims in the world, and the vast majority of them do not engage in terrorism. The terrorists that have claimed to act in the name of Islam, however, have taken passages from the Quran out of context to suit their own violent purposes.

With AI biases affecting most people who are not in a position to develop technologies, machines will continue to discriminate in harmful ways. However, striking the balance is what is needed as working towards creating systems that can embrace the full spectrum of inclusion is the end goal.

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Explained: Why Artificial Intelligences religious biases are worrying - The Indian Express

In recent months, bills to regulate the use of artificial intelligence (AI) technology in the country have been advanced in the legislature. The most advanced proposal from the chamber, written by Representative Eduardo Bismarck (PDT-CE), is ready for a vote in the House plenary. Experts considered the projects to have positive points, but said that regulation may be premature, given the speed with which AI technology is developing.

In fiction, AI is often portrayed in menacing stories, sometimes involving machines rebelling against humans. She is, for example, in films such as 2001: A Space Odyssey (1968), or The Matrix (1999). In real life, artificial intelligence is a type of computer program that is able to interpret data, learn from it and make decisions independently to accomplish a particular task set by its creator.

Today, artificial intelligence exists in a series of everyday actions. Algorithms are also found in online stores, in company inventory monitoring, in facial recognition tools, fraud prevention systems, and in analyzing consumer behavior patterns.

In the room, there are at least three other projects in addition to the one presented by Eduardo Bismarck: the proposals of Representatives Leo Moraes (Podemos-Ru), Bosco Costa (PL-SE) and Gustavo Frue (PDT-PR). They are all processed together, and attached to the Bismarck Project. In the Senate, AI is subject to three more bills. The most advanced was written by Veneziano Senator Vital do Rigo (MDB-PB) and has as rapporteur the head of government in the Senate, Eduardo Gmez (MDB-TO).

The initial version of Project Bismarck, which is being processed in the room, is simple: there are only nine articles, outlining the general principles that should govern the use of AI algorithms in Brazil. According to the proposal, this type of program should be built with respect for principles such as human dignity, protection of personal data, non-discrimination, transparency and security. Rapporteur of the proposal is Representative Louisa Kenziani (PTB-PR).

The room script also creates an artificial intelligence agent character, who can be either the developer or operator of the program. An artificial intelligence agent is legally responsible for the decisions made by the algorithm. The agent is also responsible for ensuring that the software complies with the rules of the General Data Protection Regulation (LGPD).

The Senate bill is more synthetic, with six articles. Similar to the Chambers proposal, the Venezuela text states that AI development in Brazil respects principles such as ethics, human rights, democratic values, and protection of privacy, among others. The project entered the agenda and received nearly 20 amendments, but was withdrawn so that the rapporteur could improve the final text. One amendment by Senator Paulo Baim (PT-RS), to specify that AI adoption takes into account the impact on jobs, including in the public sector.

In recent months, the House and Senate have held hearings to discuss potential regulation of artificial intelligence. Class entities representing companies in the technology sector demonstrated against the regulations. The fear is that the new rules may restrict the development of the technology, whose potential and future implications are still unknown.

Bismarck said he realized the need for the project when he saw other countries making progress on the topic, by creating laws on technology, often based on principles outlined by the Organization for Economic Co-operation and Development (OECD). We have established principles, rights and duties, to be able to help technology evolve. We are not getting into little bottlenecks. This will be left to complementary legislation later, if necessary, when the technology is more advanced, he said.

The deputy also rejects the idea that the project could hinder the development of the technology. He declared, Todays big corporations no longer want to deal with borders and rules, because they believe their compliance goes beyond local laws. But they are not sovereign nations capable of laying down these principles, as Parliament can. Our proposal is in line with the law of the United Kingdom, Singapore, Japan and the United States. Why is there no such criticism? asked the congressman.

Representatives

The room has at least four projects in the field of artificial intelligence. In addition to the proposal of Representative Eduardo Bismarck (PDT-CE), which is the most advanced, Leo Moraes (Podemos-Ru), Bosco Costa (PL-SE) and Gustavo Frue (PDT-PR) also made proposals on this topic. They all go together.

Senators

The regulation of artificial intelligence technology has also been included in three bills submitted to the Senate. What is more advanced, to date, is written by Veneziano Vital do Rgo (MDB-PB) and has as Rapporteur the Head of Government in the Senate, Eduardo Gomez (MDB-TO). In short, the text contains only six articles.

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Veneziano Vital do Rgos proposal specifies that the development of AI in Brazil respects principles such as ethics, human rights, democratic values, and protection of privacy. Similarly, the Bismarck Project states that software should be built with respect for principles such as human dignity, transparency and security.

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Artificial intelligence is on the agenda of the House and Senate - Mediarun Search

Editors note: This article is part of a multimedia series called Tomorrows Unicorns: A look inside Raleighs $1B startup pipeline, produced in conjunction withInnovate Raleigh. The series aims to spotlight some of the regions homegrown startups tipped to hit the $1-billion valuation mark, thus becoming a so-called unicorn in the language of investors, in the not-so-distant future.

RALEIGH Three years after ex-Epic Games CEO Michael Capps first launched Diveplane, a company aimed at keeping the humanity in artificial intelligence (AI), its notched a series of big wins.

In just the last year, the Raleigh-based startup landed partnerships with healthcare giants like Duke Health, and the UKs NHS Foundation Trust and BREATHE, a health data research hub.

It also closed on $3 million in new funding, bringing its total raised to around $10 million to date. Its even attracted star-studded investors, including US womens soccer stars Megan Rapinoe and Mia Hamm.

Meanwhile, Capps hinted other big deals could be in the works.

I cant speak to it yet, but were partnered with some cool organizations, he told WRAL TechWire in a Zoom call. Were lucky to sort of punch above our weight class in the industry, so Ill just leave it at that.

We had a long path of building software, he added, but now that weve started commercializing, were seeing much better uptake. Were at that wonderful phase where companies are now calling us.

While he wouldnt disclose annual revenue figures, he said: We expect to grow 3X in the next couple of months.

Could his firm be on track to becoming a $1-billion enterprise, otherwise known as a unicorn in venture capital circles?

He didnt rule it out: We have significant growth potential.

Its fastest-growing product, GEMINAI, creates a synthetic twin data set that enables sharing and analysis of highly sensitive data while protecting an individuals privacy. The new data is accurate and statistically equivalent, but omits any personal identifiers, like name or date of birth.

The uptick comes as data breaches are on the rise.

Healthcare breaches, alone, have nearly doubled since 2018 and continued to climb through the first half of 2021, according to areportby Critical Insight, a Seattle-based healthcare-focused cybersecurity firm.

Meanwhile, more than 93% of healthcare organizations experienced a data breach in the past three years (Herjavec Group).

And its costs big money.

The healthcare industry lost an estimated $25 billion to ransomware attacks in 2019 (SafeAtLast).

Data privacy affects us all, and were really seeing a shift in the market, Capps said. Its no longer enough to simply mask or anonymize. Organizations must go further to protect the most intimate of data sets, and thats what were amazing at.

Diveplanes Michael Capps and his fiance, Elizabeth Chance.

Diveplanes AI technology spun out of Hazardous Software, a company founded in 2007 by Chris Hazard, Diveplanes co-founder and chief technology officer.

Hazard holds a PhD in computer science from NC State, and worked as a software architect at Motorola and Kiva Systems.

Capps, meanwhile, is a fixture on the local Triangle startup scene. Born in Raleigh, he began his career with post-graduate degrees at UNC-Chapel Hill, MIT and the Naval Postgraduate School. Later, he spent nearly a decade as president of Epic Games, creators of mega-hit Fornite, and one of the regions early breakout unicorns, a company valued at more than $1 billion. (Today, Epic Games is estimated to be worth just shy of $30 billion.)

As his LinkedIn profile notes, his tenure included a hundred game-of-the-year awards, dozens of conference keynotes, a lifetime achievement award, and a successful free-speech defense of video games in the U.S. Supreme Court.

By 2013, Capps decided his time was up. But it didnt take long for him to sniff out his next venture.

He met Hazard through a mutual acquaintance on Raleighs startup scene, and shared the same thoughts on the future of AI and the ethical use of data.

By 2018, Diveplane was born. Among its missions: makingblack box AI,any artificial intelligence systemwhose inputs and operations are not visible to the user, easier to interpret and understand.

Big picture, we want to keep human decision-making in automated systems, Capps said. When [Hazard] finally told me about [his declassified work], I was like, You have explainable machine learning. Weve got to put this in front of everyone.

Diveplane has built what it calls the worlds first human-understandable machine-learning platform. As it boasts on its website, its tools are trainable, interpretable, and auditable.

Apart from GEMINAI, it has other products like SONAR, an anomaly detection tool to identify fraud, and ALLUVIAN, an analysis tool for the real estate market.

The name Diveplane is derived from the parts on a submarine that make it dive and surface. (Capps once taught at a Naval post-graduate school, and Hazard also worked for the Department of Defense.)

Its also metaphorically significant. AI is about searching up and down, high and low, Capps told TechWires late Alan Maurer back in 2018.

Diveplane CEO Michael Capps with his kids

Diveplane is now at an inflection point. At last count, it has 14 patents approved and another 40 patents pending. Its scaling across multiple verticals, including finance, healthcare, and defense. Another big raise is also likely on the cards, probably in the next few months.

Still, he described enterprise sales as slow and painful.

Government, intelligence officials, healthcare and finance leaders, theyre not fast to trust. [Were] like a locksmith. [Theyve got to] trust us with the jewels.

But he remains confident.If the National Security Agency is using it, and Duke is using it, its a lot easier to convince MasterCard to use it. Once we convince them, or whoever, it all falls.

Before the pandemic, Diveplane had offices in North Raleigh. But now theyre all working remotely. The team now stands at 22, and is looking to add a senior engineer and developer to its rolls.

Above all, Capps said making big profits comes secondary to his main objective: social impact.

Capps said hed eventually like to opensource Diveplanes technology.

Some of our tools, if they were free and we can afford unlimited compute, I would love to give them all away. I cant afford to do either of them; but as soon as I can, I will. Thats the goal.

NOTE: A LinkedIn Live chat with the founders is scheduled for today at 12pm. Check WRAL TechWires LinkedIn page for the live stream.

This editorial package was produced with funding support from Innovate Raleigh and other partners. WRAL TechWire retains full editorial control of all content.

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Artificial intelligence startup in Raleigh has the smarts to be a billion dollar company - WRAL Tech Wire

HERNDON,Va., Sept. 24, 2021 /PRNewswire/ --ePlus inc. (NASDAQ NGS: PLUS news)today announced that it has launched an artificial intelligence (AI) workflow technology bundle, combining hardware, software and AI implementation services, to help healthcare organizations accelerate clinical and operational AI projects from concept to production.

The AI Workflow Accelerator Bundle for Healthcare provides a complete turn-key platform for AI discovery and visualization, modeling and experimentation, productization and operations. It includes GPU-accelerated hardware from NVIDIA or Cisco with proprietary software from John Snow Labs, implementation services from ePlus and optional training and model development consulting services from SFL Scientific.

There are a growing number of highly practical uses for AI in healthcare settings, yet many organizations struggle with how to get started assessing what they need, identifying use cases and implementing the technology. According to a recent survey from KPMG, 37 percent of healthcare industry executives reported that the pace at which they are implementing AI is too slow and 47 percent responded that their organizations offer AI training courses to employees.

The AI Workflow Accelerator Bundle for Healthcare removes these and other barriers to entry by giving organizations an efficient, comprehensive package to help tailor their own solution, from hardware and software to rapid implementation, training and accelerated data consumption. The bundle takes the guesswork and uncertainty out of parsing together an AI platform by giving organizations a pre-bundled solution of technology and training components that help fast-track modeling, implementation and usage, helping them more quickly achieve success and hastening access to rich data insight.

"Utilizing this technology platform allows organizations to more confidently design, implement and begin using AI in very practical ways that will accelerate access to actionable, data-driven insight that helps to solve a variety of problems unique to healthcare environments," said Ken Farber, president of software, national partners, marketing and strategy at ePlus. "The AI Workflow Accelerator Bundle for Healthcare can serve as a powerful foundation from which organizations can build applications that help them realize operational efficiencies, enhanced patient outcomes and improved financial performance as a result of streamlined information discovery and advanced analytical capabilities. We're very excited to bring this solution to market and are proud to work with such high caliber partners to do so."

"The healthcare industry is being transformed, and the application of AI is increasingly becoming a game changer in enhancing both clinician and patient experiences," said David Talby, chief technology officer at John Snow Labs. "This bundle leverages a powerful combination of technology and services that will make it faster and easier for organizations to put AI to good use while tackling the unique compliance, terminology, and integration challenges of healthcare."

"The combination of technology and services available from ePlus, John Snow Labs, and SFL Scientific is handing organizations flexible, fast access to smart AI solutions that support the success and advancement of the healthcare industry," said Eddie Newland, director of AI services at SFL Scientific. "Developing AI solutions in highly regulated industries adds additional layers of scrutiny to an already complex task. This unique approach should allow healthcare leaders to feel confident that their organizational goals can be achieved in a compliant, secure and scalable environment that will grow as they continue to adopt AI throughout their organization."

About ePlusinc.

ePlus is a leading consultative technology solutions provider that helps customers imagine, implement, and achieve more from their technology. With the highest certifications from top technology partners and lifecycle services expertise across key areas including security, cloud, data center, collaboration, networking and emerging technologies, ePlus transforms IT from a cost center to a business enabler. Founded in 1990, ePlus has more than 1,500 associates serving a diverse set of customers in the U.S., Europe, and Asia-Pac. The Company is headquartered at 13595 Dulles Technology Drive, Herndon, VA, 20171. For more information, visit http://www.eplus.com, call 888-482-1122, or email [emailprotected]. Connect with ePlus on Facebook, LinkedIn, Twitterand Instagram. ePlus, Where Technology Means More.

ePlus, Where Technology Means More, and ePlus products referenced herein are either registered trademarks or trademarks of ePlus inc. in the United States and/or other countries. The names of other companies, products, and services mentioned herein may be the trademarks of their respective owners.

About John Snow Labs

John Snow Labs, the AI and NLP for healthcare company, provides state-of-the-art software, models, and data to help healthcare and life science organizationsbuild, deploy, and operate AI projects. The company is the developer ofSpark NLP, the world's most widely used NLP library in the enterprise, and its award-winning medical NLP software powers some of the world's leading healthcare and pharmaceutical companies. The company is the creator and host of The NLP Summit, further educating and advancing the global AI community.

About SFL Scientific

FL Scientific is a US-based data science consulting firm focused on strategy, technology, and solving business & operational challenges with Artificial Intelligence (AI). Working with clients of all sizes, industries, and AI maturity levels, our capabilities range from developing corporate AI strategy to building custom AI applications at scale. With a globally connected network of technology and cloud partners, SFL Scientific's core services include leading cross-functional efforts across business, IT, and operations. Hundreds of clientsincluding S&P100 enterprises, fastest-growing startups, and government agenciestrust SFL Scientific to create and accelerate AI initiatives.

For more information, please visit sflscientific.com and connect with us on LinkedIn & Twitter.

Statements in this press release that are not historical facts may be deemed to be "forward-looking statements." Actual and anticipated future results may vary materially due to certain risks and uncertainties, including, without limitation, the duration and impact of COVID-19 and the efficacy of vaccine roll-outs, which could materially adversely affect our financial condition and results of operations and has resulted worldwide in governmental authorities imposing numerous unprecedented measures to try to contain the virus that has impacted and may further impact our workforce and operations, the operations of our customers, and those of our respective vendors, suppliers, and partners; national and international political instability fostering uncertainty and volatility in the global economy including an economic downturn, an increase in tariffs or adverse changes to trade agreements, exposure to fluctuation in foreign currency rates, interest rates and downward pressure on prices; our ability to successfully perform due diligence and integrate acquired businesses; the possibility of goodwill impairment charges in the future; reduction of vendor incentive programs; significant adverse changes in, reductions in, or losses of relationships with one or more of our largest volume customers or vendors; the demand for and acceptance of, our products and services; our ability to adapt our services to meet changes in market developments; our ability to implement comprehensive plans to achieve customer account coverage for the integration of sales forces, cost containment, asset rationalization, systems integration and other key strategies; our ability to reserve adequately for credit losses; our ability to secure our electronic and other confidential information or that of our customers or partners and remain secure during a cyber-security attack; future growth rates in our core businesses; our ability to protect our intellectual property; the impact of competition in our markets; the possibility of defects in our products or catalog content data; our ability to adapt to changes in the IT industry and/or rapid change in product standards; our ability to realize our investment in leased equipment; our ability to hire and retain sufficient qualified personnel; and other risks or uncertainties detailed in our reports filed with the Securities and Exchange Commission. All information set forth in this press release is current as of the date of this release and ePlus undertakes no duty or obligation to update this information.

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ePlus Launches Turn-Key Technology Bundle to Facilitate Adoption of Artificial Intelligence by Healthcare Organizations - Johnson City Press...

A computer program trained to see patterns among thousands of breast ultrasound images can aid physicians in accurately diagnosing breast cancer, a new study shows.

When tested separately on 44,755 already completed ultrasound exams, the artificial intelligence (AI) tool improved radiologists ability to correctly identify the disease by 37 percent and reduced the number of tissue samples, or biopsies, needed to confirm suspect tumors by 27 percent.

Led by researchers from the Department of Radiology at NYU Langone Health and its Laura and Isaac Perlmutter Cancer Center, the teams AI analysis is believed to be the largest of its kind, involving 288,767 separate ultrasound exams taken from 143,203 women treated at NYU Langone hospitals in New York City between 2012 and 2018. The teams report publishes online September 24 in the journal Nature Communications.

Our study demonstrates how artificial intelligence can help radiologists reading breast ultrasound exams to reveal only those that show real signs of breast cancer and to avoid verification by biopsy in cases that turn out to be benign, says study senior investigator Krzysztof J. Geras, PhD.

Ultrasound exams use high-frequency sound waves passing through tissue to construct real-time images of breast or other tissues. Although not generally used as a breast cancer screening tool, it has served as an alternative to mammography or follow-up diagnostic tests for many women, says Dr. Geras, an assistant professor of radiology at NYU Grossman School of Medicine and a member of Perlmutter Cancer Center.

Ultrasound is cheaper, more widely available in community clinics, and does not involve exposure to radiation, the researchers say. Moreover, ultrasound is better than mammography for penetrating dense breast tissue and distinguishing packed but healthy cells from compact tumors.

However, the technology has also been found to result in too many false diagnoses of breast cancer, producing anxiety and unnecessary procedures for women. Some studies have shown that a majority of breast ultrasound exams indicating signs of cancer turn out to be noncancerous after biopsy.

If our efforts to use machine learning as a triaging tool for ultrasound studies prove successful, ultrasound could become a more effective tool in breast cancer screening, especially as an alternative to mammography, and for those with dense breast tissue, says study co-investigator and radiologist Linda Moy, MD. Its future impact on improving womens breast health could be profound, adds Dr. Moy, a professor of radiology at NYU Grossman School of Medicine and a member of Perlmutter Cancer Center.

Dr. Geras cautions that while his teams initial results are promising, his team only looked at past exams in their latest analysis, and clinical trials of the tool in current patients and real-world conditions are needed before it can be routinely deployed. He also has plans to refine the AI software to include additional patient information, such as a womans added risk from having a family history or genetic mutation tied to breast cancer, which was not included in their latest analysis.

For the study, more than half of ultrasound breast examinations were used to create the computer program. Ten radiologists then each reviewed a separate set of 663 breast exams, with an average accuracy of 92 percent. When aided by the AI model, their average accuracy in diagnosing breast cancer improved to 96 percent. All diagnoses were checked against tissue biopsy results.

The latest statistics from the American Cancer Society estimate that 1 in 8 women, or 13 percent of women, in the United States will be diagnosed with breast cancer over their lifetime, with more than 300,000 positive diagnoses in 2021 alone.

Funding support for the study was provided by National Institutes of Health grants P41 EB017183 and R21 CA225175; National Science Foundation grant HDR-1922658; Gordon and Betty Moore Foundation grant 9683; and Polish National Agency for Academic Exchange grant PPN/IWA/2019/1/00114/U/00001.

Besides Dr. Geras and Dr. Moy, other NYU Langone researchers involved in this study are co-lead investigators Yiqiu Artie Shen, Farah Shamout, and Jamie Oliver; and co-investigators Jan Witowski, Kawshik Kannan, Jungkyu Park, Nan Wu, Connor Huddleston, Stacey Wolfson, Alexandra Millet, Robin Ehrenpreis, Divya Awal, Cathy Tyma, Naziya Samreen, Yiming Gao, Chloe Chhor, Stacey Gandhi, Cindy Lee, Sheila Kumari- Subaiya, Cindy Leonard, Reyhan Mohammed, Christopher Moczulski, Jaime Altabet, James Babb, Alana Lewin, Beatriu Reig, and Laura Heacock.

David MarchPhone: 212-404-3528david.march@nyulangone.org

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Artificial Intelligence Tool Improves Accuracy of Breast Cancer Imaging - NYU Langone Health

Investments in 2021 will be quite beneficial if you can get these cheapest stocks of artificial intelligence. Here is a list of the cheapest artificial intelligence stocks.

Leading graphics chip company Nvidia has taken advantage of the AI boom, with its graphics cards becoming the de facto standard in data centers around the world. Machine learnings training phase demands a lot of computing power; the phase that follows, the inference phase, requires less. Graphics processing unit (GPU) chips, used primarily for rendering video games, support both phases well. Nvidias data center business represents a steadily increasing share of the companys total revenue. This segment isnt all AI-related Nvidias graphics cards are used to accelerate a wide variety of data center applications. But AI is one of the driving forces behind the companys growth. Self-driving cars are another area of focus. Nvidia develops platforms, including hardware and software, that can power driver-assistance features, as well as fully autonomous driving. A self-driving car must process massive amounts of data from multiple sensors and cameras in real-time, detect objects such as pedestrians and other vehicles, and make complex decisions. They require a tremendous amount of computing power, and thats exactly what Nvidias platform delivers. Nvidias graphics cards could someday be supplanted by more specialized processors designed for AI, but, for now, the company is in an enviable position.

This legacy tech company is an integrated provider of hardware, software, and services to large enterprise customers. Its mainframe computer systems are still ubiquitous in certain industries, and it regularly signs multi-year technology deals worth hundreds of millions of dollars each. IBMs strategy with AI is to apply the technology in ways that augment human intelligence, increase efficiency, or lower costs. In the healthcare industry, IBMs AI technology is being used to create individualized care plans, accelerate the process of bringing new drugs to market, and improve the quality of care. In the financial services industry, via the companys 2016 acquisition of promontory financial group, IBM is using AI to help clients with the daunting task of financial regulatory compliance. While the market for AI products and services is fragmented, IBM is leading the industry. Market research firm IDC ranked IBM as the leader in AI software platforms with an 8.8% market share in 2019, or US$303.8 million in revenue, up 26% from the prior year. IBM is a complicated company undergoing transformation, and AI is far from its only growth opportunity. But if youre looking to invest in a company that is well-positioned to benefit from the AI boom, then IBM is a good choice.

Micron Technology manufactures memory chips, including dynamic random-access memory (DRAM) and NAND flash memory found in solid-state storage drives. Most of what the company makes are commodity products, meaning that supply and demand dictate pricing. This leads to sometimes brutal cycles of boom and bust in the semiconductor sector, where an oversupply of chips can significantly push down prices. In 2021, demand for memory chips is strong, boosted by the growth of mobile networks, 5G, cloud computing, and a recovery in the automotive sector, and a shortage in semiconductors has helped lift prices for Microns DRAM and NAND chips. In the future, demand for memory chips will only grow, and thats especially true in the AI industry. Self-driving cars are a good example. All the sensors and cameras produce a lot of data around 1 GB per second, according to Micron estimates. Data centers running AI processes need plenty of memory and so do smartphones that may be doing AI work. Newer iPhones, for example, use AI with the camera function to produce improved images. Micron will likely remain volatile due to the nature of its business. Even though AI is driving increased demand for memory chips, in the long run, supply and demand reign supreme in the short term. If you have the stomach for a volatile stock, Micron isnt a bad way to bet on AI.

Perhaps no company is using AI more widely than Amazon. Founder and executive chairman Jeff Bezos has been an evangelist for AI and machine learning, and although Amazon started as an online retailer, technology has always been at the companys core. Today, Amazon uses artificial intelligence for everything from Alexa, its industry-leading voice-activated technology, to its Amazon Go cashier-less grocery stores, to Amazon web services Sagemaker, the cloud infrastructure tool that deploys high-quality machine learning models for data scientists and developers. Amazons e-commerce business is also built on AI since algorithms run its top-flight recommendation engines for e-commerce and video and music streaming. AI is how Amazon determines product rankings. Even Amazons logistics operations benefit from its AI prowess, which helps with scheduling, rerouting, and other ways to improve delivery accuracy and efficiency. Drone delivery, which the company has long sought to implement, would be yet another AI application for the tech giant.

C3.ai may be the closest thing on the stock market to a pure-play AI stock, as the ai in the companys name and its ticker might indicate. While the companies on the list above are diversified tech giants or chip-makers that have some businesses involved with AI, artificial intelligence is the entire focus of C3.ai. C3.ai is a SaaS company whose software allows companies to deploy large AI applications. The companys tools help its customers accelerate software development and reduce cost and risk, and they have a wide variety of applications. For example, the U.S. Air Force uses C3 AI Readiness to predict aircraft systems failures, identify spare parts, and find new ways to increase mission capability. European utility company Engie (OTC: ENGIY) is using C3 AI to analyze energy consumption and reduce energy expenditures. C3.ai is the first mover in its industry and says it isnt aware of an end-to-end enterprise AI development platform that is directly competitive with it. That unique positioning could make the company a big winner over the long term, although the AI SaaS market is evolving and could attract competition from big cloud infrastructure such as Amazon or Microsoft (NASDAQ: MSFT).

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Top Cheapest Artificial Intelligence Stocks with Big Prospects in 2021 - Analytics Insight

September 30th, 2021

9:00 AM - 10:15 AM ET

Amy Dow and Brad Thompson, Members of the Firm, speak on Shaping the Future of Artificial Intelligence (AI) Within Life Sciences, a virtual program co-hosted by Simmons & Simmons and Epstein Becker Green.

On both sides of the Atlantic, artificial intelligence (AI) is considerably transforming the health care and life sciences sector with a huge potential to advance how we research, diagnose and ultimately treat patients. Policymakers are trying to stay on top of new technologies in order to ensure the regulation keeps pace.

In this webinar, Simmons & Simmons and Epstein Becker Green join forces to discuss key regulatory considerations on AI in the European Union and the United States. The speakers notably explore the recent draft EU Regulation laying down harmonized rules on AI as well as the FDAs current regulatory landscape, its Digital Health Center of Excellence, and its AI/ML-Based Software as a Medical Device Action Plan.

Registration is complimentary, but pre-registration is required.

If you have any questions, please reach out to Dionna Rinaldi.

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[Webinar] Shaping the Future of Artificial Intelligence (AI) Within Life Sciences - September 30th, 9:00 am - 10:15 am ET - JD Supra

Investors have realized that major disruptive technologies such as AI have a high chance to thrive in the tech-driven future. Tech companies are focused on creating and manufacturing new innovations with artificial intelligence and machine learning algorithms to raise the standard of living in the global society. Thus, the demand for artificial intelligence stocks is also rising at an increasing rate. Investment in AI stocks can help to gain higher revenue instead of a massive loss because the tech stock market is not highly volatile like the cryptocurrency market. There are some ups and downs in the artificial intelligence stocks due to the impact on the demand for the COVID-19 pandemic. Some of these stocks have the potential to rise in the future despite experiencing a dip. Lets explore the top 5 AI stocks at the dip, which could rise to big heights in the future.

Splunk

Spunk is one of the tech companies that provide solutions to ensure success in the digital needs of clients. The flexible platform and purpose-built solutions scale with clients as the data and company evolves. Splunk has experienced a dip of -5.8% in its artificial intelligence stock in 2021 due to the ongoing pandemic. But the tech company is expecting a bounce in revenue in the upcoming months owing to the change in the situation. The AI stock at dip showed a downtrend for over six months but it has been in an uptrend since June 2021. Investment in AI stock is lucrative now because the current price of this artificial intelligence stock is US$149.89 with a market cap of US$24.21 billion.

Teladoc Health

Teladoc Health is known as the worlds only integrated virtual care system for delivering and empowering whole-person health. The tech company experienced a dip at the beginning of 2021 and the AI stock showed a downward trend with over 24% in February despite having positive revenue in the fourth quarter of 2020. Investors are expecting positive growth in this artificial intelligence stock with a good performance from the tech company. Teledoc Health expects to reach US$265 million with adjustments in earnings through interests and taxes. The market cap, at the beginning of 2021, was US$42 million but now it is US$22.08 billion with a current price of US$138.67.

Verastem Inc.

Verastem Inc. is known as a biopharmaceutical company that engages in the development and commercialization of drugs to cure cancer. The AI stock at dip was presented due to its capital-raising efforts. Investors are expecting a rise in one of the top artificial intelligence stocks in 2021 because the current price is US$2.99 with a market cap of US$540.47 million. Recently, the investment in the AI stock is lucrative now because the company experienced positive growth owing to its Phase FRAME study in VS-6766 for low-grade serous ovarian cancer.

Twilio Inc.

Twilio has experienced a sharp dip with a plunge ranging from 5.6% to 4.7%. The second quarter showed positive growth in revenue of US$668.90 million with an adjusted loss per share of US$0.11 despite having expectations of yielding US$598.37 million as revenue with a loss per share of US$0.13. Twilio is expanding its customer base and participating in acquisitions with top companies in the tech-driven market. The growing ecosystem of cloud-based communications tools is attracting the eyes of investors in 2021 towards the artificial intelligence stock. Twilio is one of the popular tech companies that provides a cloud-based communication platform to allow developers to operate customer engagement within the software applications across the world. The investment in AI stock is lucrative now because of the current artificial intelligence stock price of US$349 and a market cap of US$61.82 billion.

Pinterest, Inc.

Pinterest is a popular tech company that experienced an AI stock dip recently. The companys stocks have fallen to 25% in value since July 2021. The dip is anticipated to be a temporary setback for investors with a loss of 24 million users from the previous quarters. There is still a lot of revenue growth to be earned despite having a second-quarter ARPU at an 89% increase. Investors and analysts expect a rise in revenue of 53% to US$2.6 billion in 2021 with a current price of US$54.18 with a market cap of US$34.93 billion.

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Investment Alert: Top 5 Artificial Intelligence Stocks to Buy at the Dip - Analytics Insight

Data science and artificial intelligence can help prevent outbreaks COVID-19? This is the focus of the research of an Argentine project, coordinated by the Interdisciplinary Center for the Studies of Science, Technology and Innovation (Cecti), which which It was selected from more than 150 proposals from around the world and will receive funding from Canada and Sweden.

The project is called Arphai (in Argentinean English for General Research on Data Science and Artificial Intelligence for Epidemic Prevention) and its goal is to develop tools, models and recommendations that help predict and manage such epidemic events as Covid-19, but are replicable with other viruses.

The initiative originated from Ciecti a civic association set up by the National University of Quilmes (UNQ) and the Latin American College of Social Sciences (FLACSO Argentina) and was selected along with eight other proposals based in Africa, Latin America and Asia. In Latin America only two were selected: Arphai in Argentina and another project in Colombia.

Based on this recognition, it will be funded by the International Development Research Center (Idrc) in Canada and the Swedish International Development Cooperation Agency (Sida), under the Global South AI4COVID programme.

The project is coordinated by Ciecti and involves the Planning and Policy Secretariat of the Ministry of Science, Technology and Innovation and the National Information Systems Directorate of the Access to Health Secretariat of the Argentine Ministry of Health.

Researchers are also working on the initiative, Technical teams from the public administration and members of 19 institutions, including universities and research centers, in six Argentine provinces and the city of Buenos Aires.

The main goal is to develop technological tools based on artificial intelligence and data science, which are applied to electronic medical records (EHR), and allow to anticipate and detect potential epidemic outbreaks and favor preventive decision-making in the field of public health regarding Covid-19.

Among the tasks carried out, progress was also made on a pilot project to implement the electronic medical record designed by the Ministry of Health (Health History Integrated HSI) in the health networks of two municipalities on the outskirts of Buenos Aires, in order to synthesize learning and learning. Design an escalation strategy at the national level.

Another goal is to prioritize the perspective of equity, particularly gender, a criterion expressed in efforts to mitigate biases in developed prototypes (models, algorithms), in analysis and concern for the databases used and their diverse configuration. Teams: 60% of the project is made up of women, many of whom are in leadership positions.

Arphai operates under strict standards of confidentiality, protection and anonymity of data and is endorsed by the Ethics Committee of the National University of Quilmes (UNQ).

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Argentine project analyzing how data science and artificial intelligence can help prevent the outbreak of Covid-19 | Chosen from more than 150...

21-09-2021, New York, NY and Paris, France The RACE AI study conducted by Gustave Roussy and the startup Owkin, as part of the AI for Health Challenge organized by the Ile-de-France Region in 2019, was presented as a proffered paper at ESMO (European Society of Medical Oncology). This study shows that thanks to deep learning analysis applied to digitized pathology slides, artificial intelligence can classify patients with localized breast cancer between high risk and low risk of metastatic relapse in the next five years . This AI could thus become an aid to therapeutic decision making and avoid unnecessary chemotherapy and its impact on personal, professional and social lives for low risk women. This is one of the first proofs of concept illustrating the power of an AI model for identifying parameters associated with relapse that the human brain could not detect.

With 59,000 new cases per year, breast cancer ranks first among cancers in women, clearly ahead of lung cancer and colorectal cancer. It is also the cancer that causes the greatest number of deaths in women, with 14%1 of female cancer deaths in 2018,. 80%1 of breast cancers are said to be hormone-sensitive or hormone-dependent. But these cancers are extremely heterogeneous and about 20% of patients will relapse with distant metastasis.

RACE AI is a retrospective study that was conducted on a cohort of 1400 patients managed at Gustave-Roussy between 2005 and 2013 for localized hormone-sensitive (HR+, HER2-) breast cancer. These women were treated with surgery, radiotherapy, hormone therapy, and sometimes chemotherapy to reduce the risk of distant relapse.

Chemotherapy is not routinely administered because not all women will benefit from it due to a naturally favorable prognosis. The practitioner's choice is based on clinico-pathological criteria (age of the patient, size and aggressiveness of the tumor, lymph node invasion, etc.) and the decision to administer or not adjuvant chemotherapy varies between oncology centers. Genomic signatures exist today to help identify women who benefit from chemotherapy, but they are not recommended by the French National Authority for Health and are not reimbursed by the French National Health Insurance (although they are included on the RIHN reimbursement list), which makes their access and use heterogeneous in France.

Gustave Roussy and Owkin have taken up the challenge of proposing a new method that is simple, inexpensive and easy to use in all oncology centers as a therapeutic decision-making tool. Ultimately, the goal is to direct patients identified as being at high risk towards new innovative therapies and to avoid unnecessary chemotherapy for low-risk patients.

In the RACE AI study, Owkin's Data Scientists, guided by Gustave Roussy's research physicians, developed an AI model capable of reliably assessing the risk of relapse with an AUC of 81% to help the practitioner determine the benefit/risk balance of chemotherapy. This calculation is based on the patient's clinical data combined with the analysis of stained and digitized histological slides of the tumor. These slides, used daily in pathology departments by anatomo-pathologists, contain very rich and decisive information for the management of cancer. It is not necessary to develop a new technique or to equip a specific technical platform. The only essential equipment is a slide scanner, which is a common piece of equipment in laboratories. Like an office scanner that digitizes text, this scanner digitizes the morphological information present on the slide.

The results of this first study by the Owkin and Gustave Roussy teams open up strong prospects and next steps include prospectively validating the model on an independent cohort of patients treated outside Gustave Roussy. If the results are confirmed, through providing reliable information to clinicians, this AI tool will prove to be a valuable aid to therapeutic decisions.

1Institut national du cancer(France):

https://www.e-cancer.fr/Professionnels-de-sante/Les-chiffres-du-cancer-en-France/Epidemiologie-des-cancers/Les-cancers-les-plus-frequents/Cancer-du-sein

https://www.e-cancer.fr/Patients-et-proches/Les-cancers/Cancer-du-sein/Hormonotherapie

Source

ESMO 2021 Oral Session

Proffered paper: Translational research

Prediction of distant relapse in patients with invasive breast cancer from deep learning models applied to digital pathology slides

Prsentation n 1124O Channel 5 14h20-14h30 Sunday 19th Septembre 2021

Speaker : Ingrid J. Garberis, Gustave Roussy

About Gustave Roussy

Classed as the leading European Cancer Centre and the fifth on the world stage, Gustave Roussy is a centre with comprehensive expertise and is devoted entirely to patients suffering with cancer. The Institute is a founding member of the Paris Saclay Cancer Cluster. It is a source of diagnostic and therapeutic advances. It caters for almost 50,000 patients per year and its approach is one that integrates research, patient care and teaching. It is specialized in the treatment of rare cancers and complex tumors and it treats all cancers in patients of any age. Its care is personalized and combines the most advanced medical methods with an appreciation of the patients human requirements. In addition to the quality of treatment offered, the physical, psychological and social aspects of the patients life are respected. 3,200 health professionals work on its two campuses: Villejuif and Chevilly-Larue. Gustave Roussy brings together the skills, which are essential for the highest quality research in oncology: a quarter of patients treated are included in clinical trials.

For further information: http://www.gustaveroussy.fr/en, Twitter, Facebook, LinkedIn, Instagram

About Owkin

Owkin is a French-American startup that specialises in AI and Federated Learning for medical research. Owkins mission is to connect the global healthcare industry through the safe and responsible use of data and application of artificial intelligence, for faster and more effective research. Owkin was founded in 2016 by Dr Thomas Clozel M.D., a clinical research doctor and former assistant professor in clinical hematology, and Dr Gilles Wainrib, Ph.D., a pioneer in the field of artificial intelligence in biology.

Owkin leverages life science and machine learning expertise to make drug development and clinical trial design more targeted and cost effective. Owkin applies its cutting-edge machine learning algorithms across a broad network of academic medical centers, creating dynamic models that not only predicts disease evolution and treatment outcomes, but can also be used in clinical trials for enhanced analysis, high-value subgroup identification, development of novel biomarkers, and the creation of both synthetic control arms and surrogate endpoints. The end result? Better treatments for patients, developed faster, and at a lower cost.

Owkin has published several high-profile scientific achievements in top journals such as Nature Medicine, Nature Communications, Hepatology and presented results at conferences such as the American Society of Clinical Oncology.

For more information, please visit http://www.owkin.com, follow @OWKINscience on Twitter

Media contact: Talia Lliteras at Talia.Lliteras@owkin.com

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

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Artificial intelligence predicts the risk of recurrence for women with the most common breast cancer - EurekAlert