Category Archives: Artificial Intelligence
Artificial Intelligence for IT Operations: an Overview – InfoQ.com
Artificial intelligence for IT operations (AIOps) combines sophisticated methods from deep learning, data streaming processing, and domain knowledge to analyse infrastructure data from internal and external sources to automate operations and detect anomalies (unusual system behavior) before they impact the quality of service. Odej Kao, professor at the University of Technology Berlin, gave a keynote presentation about artificial intelligence for IT operations at DevOpsCon Berlin 2021.
Log data is the most powerful source of information, widely available, and can be well-processed by AI-based prediction models, as Kao explained:
In data stream processing we frequently struggle to find sufficient amounts of data. On the other hand, in AIOps we have many different sources (e.g., metric, logs, tracing, events, alerts) with several Terabytes of data produced in a typical IT infrastructure per day. We utilize the power of these hidden gems to assist DevOps administrators and jointly with the AI-models improve the availability, security, and the performance of the overall system.
According to Kao, AI-driven log analytics will be a mandatory component in future Industry 4.0, IoT, smart cities and homes, autonomous driving, data centers, and IT organizations
Most companies already have set the scene for operation of AIOPs platforms: monitoring, ELK-stacks are in place and need to be extended with AI-based analytics tools to ensure availability, performance, and security, Kao said.
Kao presented how an AIOps workflow can look:
The workflow starts with collecting data from many different sources, e.g. metric data from hardware CPU/mem/net utilization, system logs from logstash, and distributed traces from the resource manager. The hard part here is to get a holistic picture of the current infrastructure: due to virtualization, SDNs, VNFs, etc. the system is changing in short intervals, so we need to discover the current topology graph and the dependencies.
Then, we can map the recorded data to sources and activate the AIOPs pipeline, which typically consists of three steps: anomaly detection, root cause analysis, and decision-making remediation. The first two steps exploit various deep learning techniques, while the decision-making aims to automate the handling of system anomalies.
Alerting DevOps administrators is the lowest requirement. In the future, the activation of pre-defined recovery workflows or even the dynamic design of new workflows will be possible.
InfoQ interviewed Odej Kao about artificial intelligence for IT operations.
InfoQ: Whats the state of practice of AIOps?
Odej Kao: AIOps is on the rise. Many companies have already prepared the scene by installing sophisticated monitoring infrastructure, collecting and analyzing data from different sources. Especially logs have a long tradition of being used by system operators to identify problems. People are familiar with the power of this information for troubleshooting.
For example, a de facto standard for log storage and manual analysis is the ELK stack. The next logical step is to extend this infrastructure with add-ons for analytics like our logsight.ai, moogsoft or coralogix. These components take the available data, search it in real-time for anomalies, issue incident alerts and reports, and finally gather all necessary data for troubleshooting for visualisation in the company-owned, e.g. Kibana, dashboard.
The currently existing AIOPs platforms are working fine, but we need additional research and development work in terms of explainability, root cause analysis, false alarm prevention, and automatic remediation. I believe that in 2-3 years, the majority of the companies will operate AIOPs platforms simply to keep pace with the increasing data center complexity of a future IoT world.
InfoQ: Which approaches exist for AIOps and what are the main differences?
Kao: The main difference is the design of the prediction model. There are typically three different approaches balancing explainability (why a certain action was taken) vs. adaptivity (dealing with previously unknown situations and challenges).
A rule-based approach utilizes a set of rules derived from DevOps knowledge; fully explainable, but limited to the existing, pre-defined catalogue.
A supervised learning model is created by injecting failures into the system and recording the output. The corresponding input/output values serve as a learning base for the model. It works very fast, however lab systems used for injecting failures often differ from real systems in terms of noise (updates, upgrades, releases, competing applications, etc.).
An unsupervised approach assumes that the system is running smoothly for most of the time and that the number of anomalies is significantly less than normal values. Thus, the corresponding prediction model describes the normal state of the system and identifies deviations of the expected (normal) behaviour as anomalies. This approach has the best adaptivity, but the classification of the detected anomaly requires a mandatory root cause analysis execution step to detect the anomaly type.
InfoQ: How can we use AI to analyze logs, and what benefits do they bring?
Kao: Logs are the most powerful data source. They are written in the code by human developers and thus contain significant semantic information that we can exploit. They are widely available and in contrast to metric data cover the frequent changes that we see in agile development.
In large companies we see thousands of software, hardware, and configuration changes per day. Each of them is a possible source of error. The logs help us to understand the impact of changes to the overall system and to interpret the recorded data. Every update influences the prediction model and creates a "new normal". We see this in the logs and can adapt.
And only in cases where the system behaviour cannot be explained by the modification do we present the most likely log lines responsible for errors, performance degradation, or security problems. Our tool logsight.ai needs 3,5 minutes to load, pre-process, and analyse 350K log lines from production systems and to detect all 60 types of errors contained in the data. Thus, it assists the developers and operators by tremendously speeding up the troubleshooting.
The DevOps administrators do not need to scroll through thousands of unrelated log lines, but get all relevant information presented in the dashboard and can immediately start solving the detected problem. This has a significant impact on the availability, performance, and security of the system.
The analysis of logs is not limited to providing support to DevOps and troubleshooting. Analysis can also bring important contributions to other fields such as cyber security, compliance and regulations, and user experience.
InfoQ: What will the future bring us for AIOps?
Kao: I believe that AIOps platforms will be a standard component of every infrastructure. The current approach of hiring more SREs/NREs does not scale with the growing data centers and widening the scope into edge and fog computing environments.
Moreover, logs are a vital part of every autonomous system -- from large self-driving vehicles to IoT sensors in the smart cities and homes -- and serve for debugging, for detecting fraud, for improving security but also as a foundation for legal claims.
Therefore, I do not see how data centers and complex infrastructures can fulfill the future obligations without investing into AI-driven automation of such basic operations.
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Artificial Intelligence for IT Operations: an Overview - InfoQ.com
CAMPARI Creates Short Film with Artificial Intelligence Inspired by the Genius of Fellini – Broadway World
Campari, the iconic Italian aperitif, announces the return of Campari Red Diaries with Fellini Forward; a pioneering project, in collaboration with Fellini's family and former colleagues, exploring the late Federico Fellini's creative genius using new technology and machine learning to emulate the works of one of the greatest filmmakers of all time in a new and unique short film set in Rome. A one-of-a-kind documentary following the process will have its world premiere at the Venice Film Festival on September 7th and North American premiere as a Partner Presentation at the 59th New York Film Festival. The documentary will then be released on a subscription video on demand (SVOD) platform in select markets, inviting consumers to explore the future of cinema and creativity.
Since its creation over 160 years ago, Campari has pushed the boundaries of creativity to go beyond the norm, unlocking the passions and talents of artists across different fields in the path to creation. From world famous names to emerging talent, the relationship between Campari and the arts, especially cinema, has been solidified over the years. Having worked on an advertisement with Federico Fellini, for one of his few brand collaborations in 1984, this year's Campari Red Diaries Fellini Forward continues the brand's legacy, marrying both creativity and innovation within the cinema industry with the most forward-looking technology. Thanks to a team of experts from production and innovation studio UNIT9, dedicated Artificial Intelligence tools were explored and developed to unearth Federico Fellini's creative genius in ways that had never been attempted until now. Francesca Fabbri Fellini, Fellini's niece, was involved in the project from the start, together with Directors Zackary Canepari and Drea Cooper (documentary), Maximilian Niemann (short film) and a robust crew, introducing them to some of Fellini's key colleagues, sharing counsel and first-hand knowledge of her uncle, as well as contributing to the casting, costume design and script writing for the short film. This seamless collaboration between human and Artificial Intelligence showcases how the sentimental and the rational, the emotional and the data-driven can come together to create a brand new piece of art.
On the project Francesca Fabbri Fellini comments: "My Uncle Federico was original in his ways of representing life using dream-like elements as his means of communication. I think a project like this is a perfect way to honor his legacy. Though he took much inspiration from his past, he was always looking ahead. A similar approach was taken for this project with Campari; it is rooted in heritage yet is futuristic with the use of Artificial Intelligence."
Throughout the process, original members of Fellini's crew were involved and consulted, providing key insights on the Maestro's oeuvre. This included Fellini's camera operator Blasco Giurato (The Clowns, 1970), his three-time Oscar winning set designer Dante Ferretti (Orchestra Rehearsal, 1978; City of Women, 1980; And the Ship Sails On, 1983; Ginger and Fred, 1986; The Voice of the Moon, 1990) and Luigi Piccolo, Director of Sartoria Farani, a renowned Italian tailor shop which holds restored costumes from some of Fellini's greatest films including (Satyricon, 1969; The Clowns, 1970; Amarcord, 1973). Each member was conferred to define what elements in the endeavor could be perceived as Felliniesque. The result culminated in a fascinating short movie, set in the heart of Rome, that explores Fellini's life and dreams with distinctive, signature characters and arrangements throughout.
Documentary Director duo ZCDC, Zackary Canepari and Drea Cooper, captured the making of this short film, inviting significant experts of Artificial Intelligence and creativity Marcus du Sautoy and Dr. Emily L. Spratt to join the set to share their opinion on the ground-breaking initiative. Hava Aldouby, Art Historian and Fellini expert, as well as Anita Todesco, Galleria Campari curator were also consulted, providing an eclectic view on the subject, sparking debate around the role Artificial Intelligence can play in creativity and beyond.
Marcus du Sautoy, Artificial Intelligence & Creativity Expert says: "We should see Artificial Intelligence as an extraordinary collaborator; it's a new tool, like Galileo getting a telescope and being able to see further into the universe than ever before. Artificial Intelligence is a tool that allows us to analyze data at a scale that humanly, we can't possibly match. In this project, one of the Artificial Intelligence's greatest accomplishments was taking Fellini's films and analyzing each, frame by frame. Using this tool in the creative industry is an extremely exciting step forward and will assist in finding ideas that we are currently missing - Artificial Intelligence can help us break our own molds to create new stories"
Through the Campari Red Diaries 2021 Fellini Forward project, Campari aims to continue the legacy of innovation and creativity set out by its founders, inspiring future generations and creatives across the globe to unlock their own passions. Campari has also created a unique apprentice program with students involved in the Fellini Forward futuristic projects from all over the world including Centro Sperimentale di Cinematografia (CSC) in Italy, The American Film Institute in Los Angeles and The International Academy of Audio-visual Sciences (CSIA) in Switzerland to explore the creative genius of Fellini, using the Artificial Intelligence technology and speaking to key members of the film crew at each stage of production to see first-hand how human minds collaborated with Artificial Intelligence to create the short film.
Jean Jacques Dubau, Head of Marketing, Campari Group comments: "We are so pleased to be back with our Campari Red Diaries project in 2021, honoring the creative legacy of one of the greatest filmmakers of all time, Federico Fellini. At Campari we look to push the boundaries of innovation in creativity and have done so for over 160 years, with a view to leave a lasting legacy for future generations inviting them to explore their Red Passion, the urge of creativity that cannot be ignored. We hope that the endeavor of Fellini Forward will transport people in the past, and in the future, exploring creative genius in brand new ways, while enjoying an iconic Campari cocktail such as the Negroni."
Please follow Campari's social media channels for further information @CampariOfficial @CampariUSA.
https://www.youtube.com/EnjoyCampari; https://www.facebook.com/CampariUS;
https://www.instagram.com/campariusa/; https://twitter.com/campari
#Campari #RedPassion #RedDiaries #FelliniForward
To enjoy a sneak peek of Fellini Forward, view the teaser video on YouTube here.
Visit the Campari website: http://www.campari.com
Photo Credit: Courtesy of Campari
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CAMPARI Creates Short Film with Artificial Intelligence Inspired by the Genius of Fellini - Broadway World
Envision Healthcare Radiologists Harness Artificial Intelligence to Enhance Care Quality, Patient Experience – Yahoo Finance
NASHVILLE, Tenn., July 26, 2021--(BUSINESS WIRE)--Envision Healthcare, a leading national medical group, today announced that its radiologists are successfully leveraging artificial intelligence (AI) to enhance clinical evaluations and the delivery of high-quality, patient-centered care. The newly implemented AI software assists radiologists with disease detection, case prioritization and diagnosis, and has been optimized to detect three common and consequential medical emergencies: intracranial hemorrhages, pulmonary embolisms and cervical spine fractures.
This technology is being rolled out to radiologists using Envisions exclusive platform. AI software provides additional support in analyzing medical images and notifies radiologists of suspected findings to assist them in prioritizing time-sensitive conditions, such as a stroke or perforated bowel. Using AI to help enhance diagnostic accuracy and prioritize acute cases, patients can receive more timely treatment based on their condition and acuity level.
"Our radiology team does a tremendous job of reading scans to evaluate and diagnose millions of patients conditions with accuracy and timeliness," said Maria Rodriguez, MD, Chief of Radiology at Envision Healthcare. "We are continuously reviewing best practices and ways to advance the delivery of high-quality care. AI technology is one of the tools we can use to complement our clinical expertise, so we can continue achieving accurate reads and providing the right care to patients at the right time, ultimately saving their lives and improving overall health outcomes."
"AI has become invaluable, enabling radiologists to maintain and improve the quality of care we provide while meeting the growing demand for our expertise," said Chris Granville, MD, Envision Healthcare radiologist leading the medical groups AI implementation. "As one of the largest U.S. radiology groups caring for millions of patients from different backgrounds and locations, we have a highly unique and diversified dataset, which is integral to augmenting deep learning within AI. While we continue strengthening our AI application to improve our workflows and patient care, our ultimate goal is to use our dataset to help advance the AI community at large."
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Envision cares for 32 million patients every year, with its team of 800 radiologists conducting more than 10 million reads a year. Envision is uniquely positioned to improve the health of communities across the country. The medical group recently released performance data revealing it outperformed national benchmarks from the Centers for Medicare & Medicaid Services on key quality metrics for safe, timely, effective, patient-centered care in 2020. For radiology, Envisions turnaround times remained below the national standard of 30 minutes. The turnaround time for strokes, one of the most crucial diagnoses, was 27 percent lower than the national benchmark, allowing for faster cross-specialty clinical decision making, such as the administration of thrombolytics (clot busters), which leads to better recovery from strokes.
About Envision Healthcare Corporation
Envision Healthcare Corporation is a leading national medical group that delivers physician and advanced practice provider services, primarily in the areas of emergency and hospitalist medicine, anesthesiology, radiology/teleradiology, and neonatology to more than 1,800 clinical departments in healthcare facilities in 45 states and the District of Columbia. Post-acute care is delivered through an array of clinical professionals and integrated technologies which, when combined, contribute to efficient and effective population health management strategies. As a leader in ambulatory surgical care, the medical group operates and holds ownership in more than 250 surgery centers in 34 states and the District of Columbia, with medical specialties ranging from gastroenterology to ophthalmology and orthopaedics. In total, the medical group offers a differentiated suite of clinical solutions on a national scale with a local understanding of our communities, creating value for health systems, payers, providers, and patients. For additional information, visit http://www.envisionhealth.com.
View source version on businesswire.com: https://www.businesswire.com/news/home/20210726005544/en/
Contacts
Aliese PolkAliese.Polk@EnvisionHealth.com http://www.envisionhealth.com
U of T team working to address biases in artificial intelligence systems – News 1130
A University of Toronto team has launched a free service to address biases that exist in artificial intelligence (AI) systems, a technology that is increasingly used all around the world, that has the potential to have life-changing impacts on individuals.
Almost every AI system we tested has some sort of significant bias, says Parham Aarabi, a professor at the University of Toronto. For the past few years, one of the realizations has been that these AI systems are not always fair and they have different levels of bias. The challenge has been that, its been hard to know how much bias there is and what kind of bias there might be.
Earlier this year Aarabi, who has spent the last 20 years working on different kinds of AI systems, and his colleagues started HALT, a University of Toronto project launched to measure bias in AI systems, especially when it comes to recognizing diversity.
AI systems are used in many places, including in airports, by governments, health agencies, police forces, cell phones, social media aps, and in some cases by companies during hiring processes. In some cases, its as simple as walking down the street and having your face recognized.
However, its humans who design the data and systems that exist within an AI system, and thats where researchers say the biases can be created.
More and more, our interactions with the world are through artificial intelligence, Aarabi says. AI is around us and it involves us. We believe that if AI is unfair and has biases, it doesnt lead to good places so we want to avoid that.
The HALT team works with universities, companies, governments, and agencies that use AI systems. It can take them up to two weeks to perform a full evaluation, measuring the amount of bias present in the technologies, and the team can pinpoint exactly which demographics are being left out or impacted.
We can quantitatively measure how much bias there is, and from that, we can actually estimate what training data gaps there are, says Aarabi. The hope is, they can take that and improve their system, get more training data, and make it more fair.
To help their clients or partners, the team also provides a report along with guidelines on how the evaluated AI system can be improved and be made more fair.
Each case is unique, but Aarabi and his team have so far worked on 20 different AI systems and found that the number one issue has been the lack of training data for certain demographics.
If what you teach the AI is bias, for example, you dont have enough training data of all diverse inputs and individuals, then that AI does become bias, he says. Other things like the model type and being aware of what to look at and how to design AI systems, also can make an impact.
The HALT team has worked to evaluate technology which includes facial recognition, images, and even voice-based data.
We found that even dictation systems in our phones can be quite biased when it comes to dialect, Aarabi says. Native English speakers, it works reasonably well on. But if people have a certain kind of accent or different accents, then the accuracy level can drop substantially and usability of these phones becomes less.
Facial recognition has faced increased scrutiny over the years, as experts warn of the potential it has to perpetuate racial inequality. In parts of the world, the technology has been used by the criminal justice system and immigration enforcement, and there have been reports that the technology has led to the to wrongful identification and arrests of Black men in the U.S.
The American Civil Liberties Union has called for the stopping of face surveillance technologies, saying facial technology is racist, from how it was built to how it is used.
With the persisting use of these technologies, there have been calls and questions around the regulation of AI systems.
Its very important that when we use AI systems or when governments use AI systems, that there be rules in place that they need to make sure that theyre fair and validated to be fair, Aarabi says. I think slowly governments are waking up to that reality, but I do think we need to get there.
Former three-term Privacy Commissioner of Ontario, Ann Cavoukian, says most people are unaware of the consequences of AI and what its potential is in terms of positive and negatives, including biases that exist.
We found that the biases have occurred against people of colour, people of Indigenous backgrounds, she says. The consequences have to be made clear, and we have to look under the hood. We have to examine it carefully.
Earlier this year, an investigation found that the use of Clearview AIs facial-recognition technology in Canada, violated federal and provincial laws governing personal information.
In response to the investigation, it was announced that the U.S. firm would stop offering its facial-recognition services in Canada, including Clearview suspending its contract with the RCMP.
They slurp peoples images off of social media and use it without any consent or notice to the data subjects involved, says Cavoukian, who is now the Executive Director of the Global Privacy and Security by Design Centre. 3.3 billion facial images stolen, in my view, slurped from various social media sites.
Until recently, Cavoukian adds that law enforcement agencies were using the technology unbeknownst to police chiefs, most recently the RCMP. She says its important to raise awareness about what AI systems are used, and what their limitations are, particularly in their interactions with the public.
Government has to ensure that whatever it relies on for information that it acts on, is in fact accurate and thats largely missing with AI, Cavoukian says. The AI has to work equally for all of us, and it doesnt. Its bias, so how can we tolerate that.
RELATED: Canadian Civil Liberties Association has serious concerns about CCTV expansion in Ontario
Calls to address bias in AI arent only happening in Canada.
Late last month, the World Health Organization issued its first global report on Artificial Intelligence in health, saying the growing use of the technology comes with opportunities and challenges.
The technology has been used to diagnose, screen for diseases and support public health interventions in their management and response.
However, the report which includes a panel of experts appointed by WHO points out the risks of AI, including biases encoded in algorithms and the unethical collection and use of health data.
The researchers say AI systems trained to collect data from people in high-income countries, may not perform the same for others in low and middle income places.
Like all new technology, artificial intelligence holds enormous potential for improving the health of millions of people around the world, but like all technology, it can also be misused and cause harm, read a quote by Dr. Tedros Adhanom Ghebreyesus, WHOs Director-General.
This important new report provides a valuable guide for countries on how to maximize the benefits of AI, while minimizing its risks and avoiding its pitfalls.
The health agency adds that AI systems should be carefully designed and trained to reflect the diversity of socio-economic and healthcare settings. Adding that governments, providers and designers should all work together to address the ethical and human rights concerns at every level of AI systems design and development.
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U of T team working to address biases in artificial intelligence systems - News 1130
[Virtual Roundtable] NISTs Proposal on Bias in Artificial Intelligence Roundtable – July 27th, 12:00 pm – 1:00 pm ET – JD Supra
July 27th, 2021
12:00 PM - 1:00 PM ET
The effects of algorithmic and data biases continue to make headlines and erode public trust in artificial intelligence (AI). Recruiting software unfairly discriminates against women and minorities, facial recognition tools used in law enforcement misidentify specific demographic groups, and algorithms for diagnosing and treating diseases perpetuate inequalities.
In an effort to develop a framework for mitigating the risk of bias in AI, the National Institute of Standards and Technology ("NIST") has issued a proposal for identifying and managing bias in AI and is accepting comments on the proposal until September 10, 2021.
Join us on Tuesday, July 27, 2021, for a lively virtual roundtable discussion as we explore the good and the bad in NISTs proposal and the legal implications for businesses using AI tools in their operations. This roundtable will serve as a forum to help companies that are evaluating whether to file comments and what to include in their comments, as well as an opportunity to connect with peers.
We hope to see you then!
AGENDA:
12:00-12:05 p.m. Overview of NISTs Proposal12:05-12:10 p.m. The Good12:10-12:15 p.m. The Bad12:15-1:00 p.m. Peer-to-Peer Discussion with AI Roundtable Participants
WHO SHOULD ATTEND:
General Counsel and other law department leaders, leaders in human resources including Chief Talent/Acquisition Officer, and other organizational leaders such as Chief Information Officers, Chief Compliance Officers and Chief Regulatory Officers (FDA).
To ensure a quality experience for all participants, space will be limited. Registration is complimentary and pre-registration is required.
This roundtable event is only available live and will not be available at a later date.
For questions about the roundtable, please contact Matt Loomis or Dionna Rinaldi.
Members of the media, please contact Piper Hall.
Read more here:
[Virtual Roundtable] NISTs Proposal on Bias in Artificial Intelligence Roundtable - July 27th, 12:00 pm - 1:00 pm ET - JD Supra
Which companies are leading the way for artificial intelligence in the medical sector? – Verdict Medical Devices – Medical Device Network
Koninklijke Philips NV and Medtronic Plc are leading the way for artificial intelligence investment among top medical companies according to our analysis of a range of GlobalData data.
Artificial intelligence has become one of the key themes in the medical sector of late, with companies hiring for increasingly more roles, making more deals, registering more patents and mentioning it more often in company filings.
These themes, of which artificial intelligence is one, are best thought of as any issue that keeps a CEO awake at night, and by tracking and combining them, it becomes possible to ascertain which companies are leading the way on specific issues and which are dragging their heels.
According to GlobalData analysis, Koninklijke Philips NV is one of the artificial intelligence leaders in a list of high-revenue companies in the medical industry, having advertised for 578 positions in artificial intelligence, made nine deals related to the field, filed 113 patents and mentioned artificial intelligence five times in company filings between January 2020 and June 2021.
Our analysis classified 13 companies as Most Valuable Players or MVPs due to their high number of new jobs, deals, patents and company filings mentions in the field of artificial intelligence. An additional two companies are classified as Market Leaders and zero are Average Players. Five more companies are classified as Late Movers due to their relatively lower levels of jobs, deals, patents and company filings in artificial intelligence.
For the purpose of this analysis, weve ranked top companies in the medical sector on each of the four metrics relating to artificial intelligence: jobs, deals, patents and company filings. The best-performing companies the ones ranked at the top across all or most metrics were categorised as MVPs while the worst performers companies ranked at the bottom of most indicators were classified as Late Movers.
Alcon Inc is spearheading the artificial intelligence hiring race, advertising for 701 new jobs between January 2020 and June 2021. The company reached peak hiring in May 2020, when it listed 80 new job ads related to artificial intelligence.
Johnson & Johnson followed Alcon Inc as the second most proactive artificial intelligence employer, advertising for 616 new positions. Koninklijke Philips NV was third with 578 new job listings.
When it comes to deals, Koninklijke Philips NV leads with nine new artificial intelligence deals announced from January 2020 to June 2021. The company was followed by Medtronic Plc with four deals and F. Hoffmann-La Roche Ltd with three.
GlobalData's Financial Deals Database covers hundreds of thousands of M&A contracts, private equity deals, venture finance deals, private placements, IPOs and partnerships, and it serves as an indicator of economic activity within a sector.
One of the most innovative medical companies in recent months was Koninklijke Philips NV, having filed 113 patent applications related to artificial intelligence since the beginning of last year. It was followed by Olympus Corp with 31 patents and Alcon Inc with 19.
GlobalData collects patent filings from 100+ counties and jurisdictions. These patents are then tagged according to the themes they relate to, including artificial intelligence, based on specific keywords and expert input. The patents are also assigned to a company to identify the most innovative players in a particular field.
Finally, artificial intelligence was a commonly mentioned theme in medical company filings. Medtronic Plc mentioned artificial intelligence 11 times in its corporate reports between January 2020 and June 2021. Johnson & Johnson filings mentioned it 11 times and Baxter International Inc mentioned it 11 times.
Methodology:
GlobalDatas unique Job analytics enables understanding of hiring trends, strategies, and predictive signals across sectors, themes, companies, and geographies. Intelligent web crawlers capture data from publicly available sources. Key parameters include active, posted and closed jobs, posting duration, experience, seniority level, educational qualifications and skills.
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Which companies are leading the way for artificial intelligence in the medical sector? - Verdict Medical Devices - Medical Device Network
Artificial Intelligence Is On The Side Of Apes? Tesla-Fame’s AI-Based ETF Sells Facebook, Walmart And Buys AMC – Markets Insider
The Qraft AI-Enhanced US Large Cap Momentum ETF (NYSE:AMOM), an exchange-traded fund driven by artificial intelligence, has sold a majority of its holdings in Facebook Inc. (NASDAQ:FB) and Walmart Inc. (NYSE:WMT), while loading up on shares in AMC Entertainment Inc. (NYSE:AMC).
What Happened: The ETFs latest portfolio after rebalancing in early July showed that the fund has also sold major chunks ofits holdings, or entirely divested,in home retailer Home Depot Inc. (NYSE:HD), software company Adobe Inc. (NASDAQ:ADBE) and chipmaker Texas Instruments Inc. (NASDAQ:TXN).
The fund has a history of accurately predicting the price movements of electric vehicle makerTesla Inc.'s (NASDAQ:TSLA) shares.
The ETF now has online dating services provider Match Group Inc. (NASDAQ:MTCH), cybersecurity solutions company Fortinet Inc. (NASDAQ:FTNT) and auto parts retailer OReilly Automotive Inc. (NASDAQ:ORLY) as its three largest investments.
Match Group has a 3.65% weighting in the AMOM portfolio, followed by Fortinet and OReilly with 3.5% weighting each.
The other two stocks that make up the top five holdings in AMOM include auto parts retailer AutoZone Inc. (NYSE:AZO) with a 3.1% weighting and enterprise technology company Zebra Technologies Corp. (NASDAQ:ZBRA) with 2.7%.
AMC Entertainment has beenadded to the portfolio this month with a 2.34% weighting. The movie theater chain's stock is up 2078% year-to-date thanks to a short squeeze conducted by retail investors that refer to themselves as "apes."
Prior to the rebalancing, the ETF had Facebook, Walmart, Home Depot, Adobe and Texas Instruments as its five largest stock holdings.
See Also: Best Exchange Traded Funds
Why It Matters: AMOM, a product of South Korea-based fintech group Qraft, tracks 50 large-cap U.S. stocks and reweighs its holdings each month. The fund uses AI technology to automatically search for patterns that have the potential to produce excess returns and construct actively managed portfolios.
AMOM has delivered year-to-date returns of almost 15.1%, compared to its benchmark the Invesco S&P 500 Momentum ETF (NYSE:SPMO) which has returned 14.4% so far this year.
The fund said last week that it has surpassed an important milestone of $50 million in assets under management (AUM), an increase of nearly 1,500% from its $4.22 million total in August last year.
Price Action: Match Group shares closed almost 2.8% higher in Fridays trading session at $162.63, while Fortinet shares closed 1.5% higher at $256.81.
OReilly Automotive shares closed 1.7% higher in Fridays trading session at $591.65.
Read Next: 5 ETFs To Watch In The Second Half Of 2021
Photo by Samantha Celera on Flickr
How to prepare for the AI productivity boom – MIT Sloan News
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The last 15 years have brought what Stanford University professor Erik Brynjolfsson calls the productivity paradox. While theres been continuing advances in technology, such as artificial intelligence, automation, and teleconferencing tools, the U.S. and other countries have seen flagging productivity.
But a productivity boom is coming soon, Brynjolfsson said at the recent EmTech Next conference hosted by MIT Technology Review. He pointed to advances in technology, particularly artificial intelligence programs that are as good as or better than humans at some things. Businesses should now focus on incorporating the technology into work processes and preparing employees, he said, and policymakers should make sure its adoption doesnt contribute to inequality.
Brynjolfsson has been tracking the lag between introduction of artificial intelligence and corresponding productivity gains. United States productivity grew by about 1.3% in the past decade, he said, compared to more than 2.8% in the late 1990s and early 2000s. This productivity slowdown extends to other countries as well, according to research from the Organization for Economic Cooperation and Development. Brynjolfsson predicted a productivity J-curve, in which productivity declines after a technology is introduced and then rises when businesses have been able to integrate technologies into their workflow, a trajectory over time that has a J-shape.
I think were near the bottom of that J-curve right now and were about to see the takeoff, Brynjolfsson said.
Lagging productivity can be explained two main ways, Brynjolfsson said.
Mismeasurement. Productivity is traditionally measured using a countrys gross domestic product, which is based on things that are bought and sold. But many digital goods teleconferencing, smartphone apps, Wikipedia are available for free. Even though people get some benefit from these goods, they dont show up in productivity statistics. The information sectors share of the economy has barely budged since the 1980s, Brynjolfsson noted. I think most of us realize thats just not a real representation of whats going on, he said.
Happiness surveys also fail to capture the complete picture. Brynjolfsson suggested a new metric called GDP-B that would measure the benefit people gain from items. I think its far from perfect, but its a lot more precise than happiness, and I think its a lot more meaningful than GDP, he said.
Implementation and restructuring in businesses. It isnt enough to just add new technology to an organization. Companies need a complete paradigm shift. To get the full benefit, leaders need to rethink business processes, management practices, and employee skills, Brynjolfsson said.
This intangible organizational capital is essential for companies to see benefit from technological advances, but many companies put misplaced focus on technology itself.
The complete reconceptualization of a business process takes a lot. More creativity, effort, and frankly, time, than simply plugging in new technologies into old business processes, he said. We just havent been doing that in most industries.
About a decade ago, machine learning programs had about 70% accuracy, Brynjolfsson said. They have improved rapidly, to the point that they are now better than humans at identifying some things. This makes it more likely that organizations will move to integrate this technology into their business practices as entrepreneurs and managers gravitate toward these often cheaper and more efficient approaches.
We dont need any additional advances in technology to be able to have enormous effects on productivity and wages, he said.What we do need is some significant changes in business processes. We need to rethink the way work gets done.
There are signs more businesses are taking advantage of artificial intelligence programs. The 2021 AI Index report, which Brynjolfsson co-authored, found increases in not just the quality of artificial intelligence, but also business investment in the technology. The biggest increase was in the field of drug discovery and other biological uses of AI, with a 4.5% increase in investment in drug discovery in the last year.
Powerful technology is available, and every organization has an opportunity to benefit from it, he said. Successful firms will be prepared with the skills needed in the future, and leaders should focus on reskilling their workforce.
Replacing labor with capital and human work with technology brings concerns about decreased wages and increased inequality. Brynjolfssons research has documented how machine learning affects different skills and occupations, and found that there isnt one occupation where machine learning could do all the different tasks. While machine learning will likely reorganize work, it wont mean the end of work or entire occupations, he said.
But the effects will likely be uneven. The economic pie could get bigger, but that doesnt mean everyones going to benefit, Brynjolfsson said. Theres been some evidence of this happening, he said, with his research also indicating machine learning is more likely to affect low-wage occupations.
Inequality isnt inevitable, though. Brynjolfsson argued that to a large extent, it is the result of tax and education policies. He suggested three measures that companies, institutions, and policymakers can take to make sure all workers benefit from the productivity boom:
Reskilling the workforce. Taking advantage of AI and other technologies require different sets of skills. Im not just talking about more machine learning experts. Im talking about people who do more creative work, Brynjolfsson said. And while machines are able to do rote, repetitive work, companies will need people who are skilled at interpersonal, emotional connections.
Adjusting tax policy. Capital is taxed at a lower rate than labor, which might push companies to favor technology over workers. Brynjolfsson suggested leveling the playing field, or introducing measures such as earned income tax credits that help subsidize work.
Focusing on technologies that augment workers instead of replace them. Brynjolfsson said he is working on research that shows how technologists are focused on creating programs that replicate human skills. While that may be a fun goal, it actually isnt a particularly good one in terms of helping reduce inequality. It tends to drive down wages, he said. Id rather have them focused on augmenting human labor.
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How to prepare for the AI productivity boom - MIT Sloan News
Infrared cameras and artificial intelligence provide insight into boiling – MIT News
Boiling is not just for heating up dinner. Its also for cooling things down. Turning liquid into gas removes energy from hot surfaces, and keeps everything from nuclear power plants to powerful computer chips from overheating. But when surfaces grow too hot, they might experience whats called a boiling crisis.
In a boiling crisis, bubbles form quickly, and before they detach from the heated surface, they cling together, establishing a vapor layer that insulates the surface from the cooling fluid above. Temperatures rise even faster and can cause catastrophe. Operators would like to predict such failures, and new research offers insight into the phenomenon using high-speed infrared cameras and machine learning.
Matteo Bucci, the Norman C. Rasmussen Assistant Professor of Nuclear Science and Engineering at MIT, led the new work,published June 23 inApplied Physics Letters. In previous research, his team spent almost five years developing a technique in which machine learning could streamline relevant image processing. In the experimental setup for both projects, a transparent heater 2 centimeters across sits below a bath of water. An infrared camera sits below the heater, pointed up and recording at 2,500 frames per second with a resolution of about 0.1 millimeter. Previously, people studying the videos would have to manually count the bubbles and measure their characteristics, but Bucci trained a neural network to do the chore, cutting a three-week process to about five seconds. Then we said, Lets see if other than just processing the data we can actually learn something from an artificial intelligence, Bucci says.
The goal was to estimate how close the water was to a boiling crisis. The system looked at 17 factors provided by the image-processing AI: the nucleation site density (the number of sites per unit area where bubbles regularly grow on the heated surface), as well as, for each video frame, the mean infrared radiation at those sites and 15 other statistics about the distribution of radiation around those sites, including how theyre changing over time. Manually finding a formula that correctly weighs all those factors would present a daunting challenge. But artificial intelligence is not limited by the speed or data-handling capacity of our brain, Bucci says. Further, machine learning is not biased by our preconceived hypotheses about boiling.
To collect data, they boiled water on a surface of indium tin oxide, by itself or with one of three coatings: copper oxide nanoleaves, zinc oxide nanowires, or layers of silicon dioxide nanoparticles. They trained a neural network on 85 percent of the data from the first three surfaces, then tested it on 15 percent of the data of those conditions plus the data from the fourth surface, to see how well it could generalize to new conditions. According to one metric, it was 96 percent accurate, even though it hadnt been trained on all the surfaces. Our model was not just memorizing features, Bucci says. Thats a typical issue in machine learning. Were capable of extrapolating predictions to a different surface.
The team also found that all 17 factors contributed significantly to prediction accuracy (though some more than others). Further, instead of treating the model as a black box that used 17 factors in unknown ways, they identified three intermediate factors that explained the phenomenon: nucleation site density, bubble size (which was calculated from eight of the 17 factors), and the product of growth time and bubble departure frequency (which was calculated from 12 of the 17 factors). Bucci says models in the literature often use only one factor, but this work shows that we need to consider many, and their interactions. This is a big deal.
This is great, says Rishi Raj, an associate professor at the Indian Institute of Technology at Patna, who was not involved in the work. Boiling has such complicated physics. It involves at least two phases of matter, and many factors contributing to a chaotic system. Its been almost impossible, despite at least 50 years of extensive research on this topic, to develop a predictive model, Raj says. It makes a lot of sense to us the new tools of machine learning.
Researchers have debated the mechanisms behind the boiling crisis. Does it result solely from phenomena at the heating surface, or also from distant fluid dynamics? This work suggests surface phenomena are enough to forecast the event.
Predicting proximity to the boiling crisis doesnt only increase safety. It also improves efficiency. By monitoring conditions in real-time, a system could push chips or reactors to their limits without throttling them or building unnecessary cooling hardware. Its like a Ferrari on a track, Bucci says: You want to unleash the power of the engine.
In the meantime, Bucci hopes to integrate his diagnostic system into a feedback loop that can control heat transfer, thus automating future experiments, allowing the system to test hypotheses and collect new data. The idea is really to push the button and come back to the lab once the experiment is finished. Is he worried about losing his job to a machine? Well just spend more time thinking, not doing operations that can be automated, he says. In any case: Its about raising the bar. Its not about losing the job.
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Infrared cameras and artificial intelligence provide insight into boiling - MIT News
The smart role of Artificial Intelligence in todays world – BL on Campus
Artificial Intelligence (AI) has been redefining society in ways we have never anticipated. Technology is clinging to us in every walk of our lives, right from unlocking our smartphones to our day-to-day activities, online shopping, intelligent car dashboards, autonomous robots and so on. Though the concept of AI was first talked about in the early 1950s, forming a basis for many computer learning and complex decision-making processes, it is only of late, where processing huge amounts of data is required, that this field of technology is picking up pace.
What is in the AI basket?
AI is not a technology, rather it is a science or field of study. It is a constellation that encompasses a lot of statistical computation methods, pre and post analyses techniques for handling structured and unstructured data. It is an interesting endeavour of replicating and stimulating human intelligence through machine and deep learning platforms, natural language generation, virtual agents, text-voice-image recognition, AI optimised hardware, robotic process automation, cognitive search system and so on. It has a goal of utilising all the technologies to make intelligent machines.
Growth of AI in India
AI is the tool of innovation being experimented with, in almost all Indian domains, including healthcare, education, agriculture, finance, automobiles, energy, retail, manufacturing, scientific research with autonomous discoveries in place. In India, companies like Walmart, Google, Microsoft, Amazon, Samsung are into AI-based research and product offerings. Still, our country has a lot of potential to expand its research in this cutting-edge technology. Most of our educational, government and private institutes cradle and motivate AI researchers, innovations and start-ups.
The Government is pushing the private sector and offers many opportunities through DST, Niti Aayog, IndiaAI and many more, to create innovative technological solutions and fund AI-based start-ups. The start-ups are focussed in the cities like Bengaluru, Hyderabad, Ahmedabad, Mumbai, Delhi for AI-based businesses.
Why AI matters in todays scenario
AI, which emerged from the research world as a proof-of-concept has been strategically scaling up due to the pace of digitisation. AI is favoured for its large data processing, end-to-end efficiency of decoding complex processes, improved accuracy and help in decision-making, intelligent offerings, smart services - content, task automation and so on. We can see its overwhelming development in healthcare, pharmaceutical, scientific research, and e-commerce.
The interactive applications of Google, DeepMinds Alpha Fold, BenevolentAI, chatbots such as Clara and Zini; Aryoga Setu, Co-Win, Amazon, Zomato, Swiggy are among the few proving to be our pandemic tech saviours.
Impact of AI in business
Business over the years has evolved from local corner shops to the booming online shopping platforms. These modernised techniques not only make individual lives easier, but also streamline business processes for improving consumer experience, sales forecasting and automated decision making to meet business goals. Businesses work well when humans, machines and technologies integrate for each others benefit. Todays business world is solely dependent on AI, Cloud, Big Data technologies of which e-commerce and m-commerce are the mainstream, having a great business impact globally. In synch with global developments in innovation and automation, India too has brought about a digital transformation over the last two decades. Now, technological developments have gained pace more than what has been predicted; the pandemic played a great role in its quick transformation and adoption.
How to look for jobs in AI?
Today, Artificial Intelligence is a lucrative domain, promising job growth in a competitive IT industry. Four out of five C-suite executives believe that they need to speed up data processing and automation, if they have to survive in their business. So, recruiters look for advanced technical skills, extensive practical experience. AI skills secure the top place among the fastest growing job profiles over the recent years. The prominent job roles include big data engineer, business intelligence developer, data scientist, data analyst, cyber analyst and expert, AI-Deep learning-machine learning engineer, computer vision specialist along with equivalent research jobs.
How can one find a good job in AI? The answer is, there are several avenues and opportunities to be had by connecting with experts via LinkedIn, technical blogs, career fairs and company career sites. The tech talks given by companies in university, conclaves hosting academics-government-industry groups will help you understand the actual employment needs and goals. Always aim to seek opportunities at government and industry-funded research labs during the early years of your higher education. This will help you to nurture your skillset to the best. Work for open-source and stack overflow contributions which will add value to your technical profile. Technical competitions like hackathons/ideathons/makeathons will upskill your innovative ideas along with the required life skills.
Globally, today we are in a challenging situation. All, irrespective of the sectors, are working on the revamp strategy to balance the economy post Covid-19. AI will endeavour to revive the profitability and development of industry . New and advanced opportunities are expected to open up.
AI is and will be driving a promising future in the new normal. It will be the main driver for emerging and new technologies. So, take an interdisciplinary approach to hone your skills in an ever-evolving field. Think big, start small, act fast.
(The writer is Professor & Chairperson, School of Computing, SRM Institute of Science and Technology.)
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The smart role of Artificial Intelligence in todays world - BL on Campus