Category Archives: Artificial Intelligence

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.

Original post:

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

Urgent action is needed as it can take time to assess and address the serious risks this technology poses to human rights, warnedtheHigh Commissioner:The higher the risk for human rights, the stricter the legal requirements for the use of AI technology should be.

Ms. Bachelet also called for AI applications that cannot be used in compliance with international human rights law,to be banned. Artificial intelligence can be a force for good, helping societies overcome some of the great challenges of our times. But AI technologies can have negative, even catastrophic, effects if they areused without sufficient regard to how they affect peoples human rights.

On Tuesday, the UN rights chiefexpressed concern about the "unprecedented level of surveillance across the globe by state and private actors", which she insisted was "incompatible" with human rights.

She wasspeakingat a Council of Europe hearing on the implications stemming fromJulyscontroversy over Pegasus spyware.

The Pegasus revelations were no surprise to many people, Ms. Bachelet told the Council of Europe's Committee on Legal Affairs and Human Rights, in reference to the widespread use of spyware commercialized by the NSO group, which affected thousands of people in 45 countries across four continents.

The High Commissioners call came asher office, OHCHR,published a report that analyses how AI affects peoples right to privacy and other rights, including the rights to health, education, freedom of movement, freedom of peaceful assembly and association, and freedom of expression.

The document includes an assessment of profiling, automated decision-making and other machine-learning technologies.

The situation is dire said Tim Engelhardt, Human Rights Officer, Rule of Law and Democracy Section, who was speaking at the launch of the report in Geneva on Wednesday.

The situation has not improved over the yearsbut has become worsehe said.

Whilst welcoming the European Unions agreement to strengthen the rules on control and the growth of international voluntary commitments and accountability mechanisms, he warned that we dont think we will have a solution in the coming year, butthe first steps need to be taken now or many people in the world will pay a high price.

OHCHRDirector of Thematic Engagement, Peggy Hicks,added to Mr Engelhardts warning, stating it's not about the risks in future, but the reality today.Without far-reaching shifts,the harms will multiply with scale and speed and we won't know the extent of the problem.

According to the report, States and businesses often rushed to incorporate AI applications, failing to carry out due diligence. It states that there have been numerous cases of people being treated unjustlydue toAImisuse, such as being denied social security benefits because of faulty AI tools or arrested because of flawed facial recognitionsoftware.

The document details how AI systems rely on large data sets, with information about individuals collected, shared, merged and analysed in multiple and often opaque ways.

The data used to inform and guide AI systems can be faulty, discriminatory, out of date or irrelevant, it argues, adding that long-term storage of data also poses particular risks, as data could in the future be exploited in as yet unknown ways.

Given the rapid and continuous growth of AI, filling the immense accountability gap in how data is collected, stored, shared and used is one of the most urgent human rights questions we face, Ms. Bachelet said.

The report also stated that serious questions should be raised about the inferences, predictions and monitoring by AI tools, including seeking insights into patterns of human behaviour.

It found that the biased datasets relied on by AI systems can lead to discriminatory decisions, which are acute risks for already marginalized groups. This is whythere needs to be systematic assessment and monitoring of the effects of AI systems to identify and mitigate human rights risks,she added.

An increasingly go-to solution for States, international organizations and technology companies are biometric technologies, which the report states are an area where more human rights guidance is urgently needed.

These technologies, which include facial recognition, are increasingly used to identify people in real-time and from a distance, potentially allowing unlimited tracking of individuals.

The report reiterates calls for a moratorium on their use in public spaces, at least until authorities can demonstrate that there are no significant issues with accuracy or discriminatory impacts and that these AI systems comply with robust privacy and data protection standards.

The document also highlights a need for much greater transparency by companies and States in how they are developing and using AI.

The complexity of the data environment, algorithms and models underlying the development and operation of AI systems, as well as intentional secrecy of government and private actors are factors undermining meaningful ways for the public to understand the effects of AI systems on human rights and society, the report says.

We cannot afford to continue playing catch-up regarding AI allowing its use with limited or no boundaries or oversight and dealing with the almost inevitable human rights consequences after the fact.

The power of AI to serve people is undeniable, but so is AIs ability to feed human rights violations at an enormous scale with virtually no visibility. Action is needed now to put human rights guardrails on the use of AI, for the good of all of us, Ms. Bachelet stressed.

See more here:

Urgent action needed over artificial intelligence risks to human rights - UN News

During the recent Tesla AI Day event, Elon Musk said he discourages machine learning, because it is really difficult. Unless you have to use machine learning, dont do it.

Well, Musk may be right in his assessment, because machine learning is quite difficult to implement. Most companies desire the benefits of what artificial intelligence can achieve for their business, but most dont have what it takes to get it up and running. Therefore, as much as 85% of ML projects currently fail.

The takeaway from Musks startling statement is that organizations cant treat AI, of which machine learning is a subset, like a part-time project. Many businesses are making some important mistakes when trying to do AI. But it doesnt have to be this way. Below are five data points from Bin Zhao, Ph.D., Lead Data Scientist at Datatron, showing some common mistakes of AI implementation.

Dont treat AI/ML development like traditional software development. Developing AI/ML models is a much different process than software development, but many organizations try to apply the traditional software development lifecycle to manage AI/ML models.

Machine Learning development lifecycle (MLLC) takes much more time because of additional factors including translating AI algorithms to compatible software codes, unique infrastructure requirements, the need for frequent model iterations, and more. Compared to traditional programming languages, it can take more than five times as long. This means todays typical application release processes are simply not applicable.

This type of tools mistake introduces unnecessary delays and inefficiencies. In most IT situations, organizations can control the types of servers they buy, the software tools they use, the dependencies they build with and so on.

Not so with AI/ML; organizations must allow their data scientists to use their preferred tools based on what they think will get the job done in the best way. Otherwise, theyre likely to see all their data scientists leave.

DevOps is the union of software development and operations with the goals of reducing solution delivery time and sustaining a good user experience through automation (e.g. CI/CD and monitoring). But DevOps experts dont know the nuances of working with ML models.

MLOps is a new term that expresses how to apply DevOps rules to automate the building, testing and deployment of ML systems. The goal of MLOps is to unite ML application development and the operation of ML applications, making it easier for groups to deploy finer models more often.

Data scientists need the right raw data for modeling, and they excel in uncovering data to build the best models to solve business challenges. However, that does not mean they are experts in all the intricacies of deploying models to work with existing applications and infrastructure. This causes friction between them and the engineering team and business leaders, resulting in low job satisfaction for data scientists.

Though highly skilled and trained, they must rely on others for deployment and production, which also means that they cant iterate rapidly. And since the projects shift to the engineering team, who dont have the ML skill set, its easy for them to miss details especially if the model is not making accurate predictions.

Academic AI research has historically focused on developing models and algorithms. Limited efforts have been devoted towards iterating and improving data sets for a specific business problem, operationalizing a machine learning model or monitoring models in production.

Building and deploying a machine learning model for solving a real world problem is much more than developing the algorithm itself.

Operationalizing ML models is hard but not impossible. Using a new model development life cycle will streamline the process of model development and model production. It does this by helping data scientists, engineering and other involved teams make effective decisions in a timely manner. It will also help teams to reduce production risks.A successful model governance tool can also help by standardizing processes, simplifying governance and significantly reducing risks.

About the Author:

Bin Zhao, Ph.D., Lead Data Scientist at Datatron

View post:

Elon is Right, AI is Hard: Five Pitfalls to Avoid in Artificial Intelligence | eWEEK - eWeek