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Category Archives: Machine Learning

Machine Learning and AI – What Does The Future Hold? – Analytics Insight

In data, companies trust. By 2021, one in four forward-thinking enterprises will push AI to new frontiers, such as holographic meetings for remote work and on-demand personalised manufacturing, as per new predictions by Forrester Research. Even today, all of us are subconsciously using Machine Learning in our daily lives. Wish to travel? Maps: AI-powered. Wish to stay home and yet be social? Facebook, Snapchat: ML-AI powered.A nascent domain thats roughly 60 years old, has changed the way humans and machines perform, thats for sure.

AI will create 2.3 million jobs in 2020. By 2020, Artificial Intelligence to create more jobs than it eliminates, says Gartner. Todays tech-ready industries already use AI for automated jobs that are highly repeatable, where large quantities of observations and decisions can be analysed for patterns.

To stay relevant and secure an irreplaceable position in your industry, it is important to upskill and be in the know of the latest trends and technologies. The first step in doing so, would be to pursue online programs that allow you to work while you learn. It is extremely crucial to keep in mind that only listening to professors half-mindedly while bingeing in a parallel tab will not cut it. The program that you choose to pursue needs to be as rigorous and engaging as any offline university that you go to. upGrad, Indias largest online higher education company, has collaborated with top national and global universities like IIIT Bangalore, IIT Madras, and Liverpool John Moores University to deliver online Machine Learning programs to working professionals. These programs are 100% online and cover industry-relevant case studies and projects, allowing learners to get practical knowledge along with theoretical comprehension, thanks to best-in-class content and live lectures from industry leaders. Based on your interest, you can choose a format of your choice, be it a PG Diploma, an Advanced Certification, or a Masters degree. With one-on-one mentorship from industry leaders and personalised assistance from dedicated student mentors, upGrad ensures that every learner hits the ground running, as soon as they graduate.

Though the global pandemic is affecting millions of jobs worldwide, according to Indeed, a leading job portal, the demand for AI jobs in India has been on the upswing for five years, and has particularly increased in the past six months. Python, a programming language and Natural Language Processing (NLP), essential to making Artificial Intelligence effective as it is the study of computer and human language interaction are the most high demand skills within AI jobs.*With a rise in demand, the competition rises as well. Stay ahead in your career, online programs from top universities are a few clicks away (thanks to Machine Learning and AI!).

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Artificial Intelligence Advances Showcased at the Virtual 2020 AACC Annual Scientific Meeting Could Help to Integrate This Technology Into Everyday…

CHICAGO, Dec. 13, 2020 /PRNewswire/ -- Artificial intelligence (AI) has the potential to revolutionize healthcare, but integrating AI-based techniques into routine medical practice has proven to be a significant challenge. A plenary session at the virtual 2020 AACC Annual Scientific Meeting & Clinical Lab Expo will explore how one clinical lab overcame this challenge to implement a machine learning-based test, while a second session will take a big picture look at what machine learning is and how it could transform medicine.

Machine learning is a type of AI that uses statistics to find patterns in massive amounts of data. It could launch healthcare into a new era by mining medical data to find cures for diseases, identify vulnerable patients before they become ill, and better personalize testing and treatments. In spite of this technology's promise, though, the medical community continues to grapple with numerous barriers to adoption, and in the field of laboratory medicine in particular, very few machine learning tests are currently offered as part of regular care.

A 10-year machine learning project undertaken by Ulysses G.J. Balis, MD, and his colleagues at the University of Michigan in Ann Arbor could help to change this by providing a blueprint for other healthcare institutions looking to harness AI. As Dr. Balis will discuss in his plenary session, his institute developed and implemented a machine learning test called ThioMon to guide treatment of inflammatory bowel disease (IBD) with azathioprine. With an approximate cost of only $20 a month, azathioprine is much cheaper than other IBD medications (which can cost thousands of dollars a month), but its dosage needs to be finetuned for each patient, making it difficult to prescribe. ThioMon solves this issue by analyzing a patient's routine lab test results to determine if a particular dose of azathioprine is working or not.

Balis's team found that the test performs just as well as a colonoscopy, which is the current gold standard for assessing IBD patient response to medication. Even more exciting is that clinical labs could use ThioMon's general approachanalyzing routine lab test results with machine learning algorithmsto solve any number of other patient care challenges.

"There are dozens, if not hundreds of additional diagnoses that we can extract from the routine lab values that we've been generating for decades," said Dr. Balis. "This lab data is, in essence, a gold mine, and the development of these machine learning tools marks the start of a new gold rush."

One of the additional conditions that this machine learning approach can diagnose is, in fact, COVID-19. In the session, "How Clinical Laboratory Data Is Impacting the Future of Healthcare?" Jonathan Chen, MD, PhD, of Stanford University, and Christopher McCudden, PhD, of the Eastern Ontario Regional Laboratory Association, will touch on a new machine learning test that analyzes routine lab test results to determine if patients have COVID-19 even before their SARS-CoV-2 test results come back. As COVID-19 cases in the U.S. reach record highs, this test could enable labs to diagnose COVID-19 patients quickly even if SARS-CoV-2 test supply shortages worsen or if SARS-CoV-2 test results become backlogged due to demand.

Beyond this, Drs. Chen and McCudden plan to give a bird's eye view of what machine learning is, how it works, and how it can improve efficiency, reduce costs, and improve patient outcomesparticularly by democratizing patient access to medical expertise.

"Medical expertise is the scarcest resource in the healthcare system," said Dr. Chen, "and computational, automated tools will allow us to reach the tens of millions of people in the U.S.and the billions of people worldwidewho currently don't have access to it."

Machine Learning Sessions at the 2020 AACC Annual Scientific MeetingAACC Annual Scientific Meeting registration is free for members of the media. Reporters can register online here:https://www.xpressreg.net/register/aacc0720/media/landing.asp

Session 14001: Between Scylla and Charybdis: Navigating the Complex Waters of Machine Learning in Laboratory Medicine

Session 34104: How Clinical Laboratory Data Is Impacting the Future of Healthcare?

Abstract A-005: Machine Learning Outperforms Traditional Screening and Diagnostic Tools for the Detection of Familial Hypercholesterolemia

About the 2020 AACC Annual Scientific Meeting & Clinical Lab ExpoThe AACC Annual Scientific Meeting offers 5 days packed with opportunities to learn about exciting science from December 13-17, all available on an online platform. This year, there is a concerted focus on the latest updates on testing for COVID-19, including a talk with current White House Coronavirus Task Force testing czar, Admiral Brett Giroir. Plenary sessions include discussions on using artificial intelligence and machine learning to improve patient outcomes, new therapies for cancer, creating cross-functional diagnostic management teams, and accelerating health research and medical breakthroughs through the use of precision medicine.

At the virtual AACC Clinical Lab Expo, more than 170 exhibitors will fill the digital floor with displays and vital information about the latest diagnostic technology, including but not limited to SARS-CoV-2 testing, mobile health, molecular diagnostics, mass spectrometry, point-of-care, and automation.

About AACCDedicated to achieving better health through laboratory medicine, AACC brings together more than 50,000 clinical laboratory professionals, physicians, research scientists, and business leaders from around the world focused on clinical chemistry, molecular diagnostics, mass spectrometry, translational medicine, lab management, and other areas of progressing laboratory science. Since 1948, AACC has worked to advance the common interests of the field, providing programs that advance scientific collaboration, knowledge, expertise, and innovation. For more information, visit http://www.aacc.org.

Christine DeLongAACCSenior Manager, Communications & PR(p) 202.835.8722[emailprotected]

Molly PolenAACCSenior Director, Communications & PR(p) 202.420.7612(c) 703.598.0472[emailprotected]

SOURCE AACC

http://www.aacc.org

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Artificial Intelligence Advances Showcased at the Virtual 2020 AACC Annual Scientific Meeting Could Help to Integrate This Technology Into Everyday...

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Research Associate in Computer Vision and Machine Learning for Robotics job with UNIVERSITY OF LINCOLN | 238417 – Times Higher Education (THE)

School of Computer Science

Location: LincolnSalary: From 33,797 per annumThis post is full time and fixed term until 13 August 2021Closing Date: Sunday 10 January 2021Interview Date: Thursday 28 January 2021Reference: COS707B

The University of Lincoln is seeking to appoint a Research Associate. The position is funded by the Ceres Agri-Tech Knowledge Exchange Partnership, which aims to build a second-generation robotic with advanced stereovision in conjunction with a novel high tack surface gripper/end effector.

In our previous project, a UoL team, which included LMF Mushrooms and Stelram Engineering, successfully built a picking prototype robot that can pick individual upright mushrooms with minimal damage. The system was operated by a combination of novel soft robotic actuators and an advanced tracking system driven by powerful 3D perception algorithms. The problem that this project will try to solve is picking mushrooms that grow in highly complex and biologically variable clusters. There is a lack of a simple universal grasping actuator to pick mushrooms without damage, as well as the need to develop powerful 3D perception algorithms to target mushrooms and to integrate this into motion planning and control systems. This project will attempt to solve these issues by highly novel soft robotic actuators deployed in combination with advanced guidance and tracking systems operating within a 3D vision sensed environment.

This project has the potential to change the mushroom sector and the application of soft robotics combined with novel tracking algorithms has the capability to underpin the wider deployment of RAS in multiple sectors of food and manufacturing.

We are looking to recruit a postdoctoral Research Associate specialised in the following:

The successful candidate will contribute to the University's ambition to achieve international recognition as a research-intensive institution and will be expected to design, conduct and manage original research in the above subject areas as well contribute to the wider activities of Lincoln School of Computer Science. Evidence of authorship of research outputs of international standing is essential, as is the ability to work collaboratively as part of a team, including excellent written and spoken communication skills. Opportunities to mentor and co-supervise PhD students working in the project team will also be available to outstanding candidates.

Informal enquiries about the post can be made to Dr Bashir Al-Diri (email: baldiri@lincoln.ac.uk).

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Research Associate in Computer Vision and Machine Learning for Robotics job with UNIVERSITY OF LINCOLN | 238417 - Times Higher Education (THE)

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Apple’s SVP of Machine Learning & AI John Giannandrea has been assigned to Oversee Apple’s Secretive ‘Project Titan’ – Patently Apple

Patently Apple has been covering the latest Project Titan patents for years, including a granted patent report posted this morning covering another side of LiDAR that was never covered before. While some in the industry have doubted Apple will ever do anything with this project, Apple has now reportedly moved its self-driving car unit under the leadership of top artificial intelligence executive John Giannandrea, who will oversee the companys continued work on an autonomous system that could eventually be used in its own car.

Bloomberg's Mark Gurman is reporting today that Project Titan is run day-to-day by Doug Field. His team of hundreds of engineers have moved to Giannandreas artificial intelligence and machine-learning group, according to people familiar with the change.

Previously, Field reported to Bob Mansfield, Apples former senior vice president of hardware engineering. Mansfield has now fully retired from Apple, leading to Giannandrea taking over. Mansfield oversaw a shift from the development of a car to just the underlying autonomous system.

In 2017, Patently Apple posted a report titled "Apple's CEO Confirms Project Titan is the 'Mother of all AI Projects' Focused on Self-Driving Vehicles." For more read the full Bloomberg report.

Like with all major Apple projects, be it for a head-mounted display device, smartglasses, folding devices, Apple keeps its secrets and prototypes under wraps until they've holistically worked out their roadmap.

That's why following Apple's patents is the best way to keep on top of the technology that Apple's engineers are actually working on in some capacity within the various ongoing projects. Review our Project Titan patent archive to see what Apple has been working on.

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Apple's SVP of Machine Learning & AI John Giannandrea has been assigned to Oversee Apple's Secretive 'Project Titan' - Patently Apple

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8 Leading Women In The Field Of AI – Forbes

These eight women are at the forefront of the field of artificial intelligence today. They hail from ... [+] academia, startups, large technology companies, venture capital and beyond.

It is a simple truth: the field of artificial intelligence is far too male-dominated. According to a 2018 study from Wired and Element AI, just 12% of AI researchers globally are female.

Artificial intelligence will reshape every corner of our lives in the coming yearsfrom healthcare to finance, from education to government. It is therefore troubling that those building this technology do not fully represent the society they are poised to transform.

Yet there are many brilliant women at the forefront of AI today. As entrepreneurs, academic researchers, industry executives, venture capitalists and more, these women are shaping the future of artificial intelligence. They also serve as role models for the next generation of AI leaders, reflecting what a more inclusive AI community can and should look like.

Featured below are eight of the leading women in the field of artificial intelligence today.

Joy Buolamwini has aptly been described as the conscience of the A.I. revolution.

Her pioneering work on algorithmic bias as a graduate student at MIT opened the worlds eyes to the racial and gender prejudices embedded in facial recognition systems. Amazon, Microsoft and IBM each suspended their facial recognition offerings this year as a result of Buolamwinis research, acknowledging that the technology was not yet fit for public use. Buolamwinis work is powerfully profiled in the new documentary Coded Bias.

Buolamwini stands at the forefront of a burgeoning movement to identify and address the social consequences of artificial intelligence technology, a movement she advances through her nonprofit Algorithmic Justice League.

Buolamwini on the battle against algorithmic bias: When I started talking about this, in 2016, it was such a foreign concept. Today, I cant go online without seeing some news article or story about a biased AI system. People are just now waking up to the fact that there is a problem. Awareness is goodand then that awareness needs to lead to action. That is the phase that were in.

From SRI to Google to Uber to NVIDIA, Claire Delaunay has held technical leadership roles at many of Silicon Valleys most iconic organizations. She was also co-founder and engineering head at Otto, the pedigreed but ill-fated autonomous trucking startup helmed by Anthony Levandowski.

In her current role at NVIDIA, Delaunay is focused on building tools and platforms to enable the deployment of autonomous machines at scale.

Delaunay on the tradeoffs between working at a big company and a startup: Some kinds of breakthroughs can only be accomplished at a big company, and other kinds of breakthroughs can only be accomplished at a startup. Startups are very good at deconstructing things and generating discontinuous big leaps forward. Big companies are very good at consolidating breakthroughs and building out robust technology foundations that enable future innovation.

Rana el Kaliouby has dedicated her career to making AI more emotionally intelligent.

Kaliouby is credited with pioneering the field of Emotion AI. In 2009, she co-founded the startup Affectiva as a spinout from MIT to develop machine learning systems capable of understanding human emotions. Today, the companys technology is used by 25% of the Fortune 500, including for media analytics, consumer behavioral research and automotive use cases.

Kaliouby on her big-picture vision: My lifes work is about humanizing technology before it dehumanizes us.

Daphne Kollers wide-ranging career illustrates the symbiosis between academia and industry that is a defining characteristic of the field of artificial intelligence.

Koller has been a professor at Stanford since 1995, focused on machine learning. In 2012 she co-founded education technology startup Coursera with fellow Stanford professor and AI leader Andrew Ng. Coursera is today a $2.6 billion ed tech juggernaut.

Kollers most recent undertaking may be her most ambitious yet. She is the founding CEO at insitro, a startup applying machine learning to transform pharmaceutical drug discovery and development. Insitro has raised roughly $250 million from Andreessen Horowitz and others and recently announced a major commercial partnership with Bristol Myers Squibb.

Koller on advice for those just starting out in the field of AI: Pick an application of AI that really matters, that is really societally worthwhilenot all AI applications areand then put in the hard work to truly understand that domain. I am able to build insitro today only because I spent 20 years learning biology. An area I might suggest to young people today is energy and the environment.

Few individuals have left more of a mark on the world of AI in the twenty-first century than Fei-Fei Li.

As a young Princeton professor in 2007, Li conceived of and spearheaded the ImageNet project, a database of millions of labeled images that has changed the entire trajectory of AI. The prescient insight behind ImageNet was that massive datasetsmore than particular algorithmswould be the key to unleashing AIs potential. When Geoff Hinton and team debuted their neural network-based model trained on ImageNet at the 2012 ImageNet competition, the modern era of deep learning was born.

Li has since become a tenured professor at Stanford, served as Chief Scientist of AI/ML at Google Cloud, headed Stanfords AI lab, joined the Board of Directors at Twitter, cofounded the prominent nonprofit AI4ALL, and launched Stanfords Human-Centered AI Institute (HAI). Across her many leadership positions, Li has tirelessly advocated for a more inclusive, equitable and human approach to AI.

Li on why diversity in AI is so important: Our technology is not independent of human values. It represents the values of the humans that are behind the design, development and application of the technology. So, if were worried about killer robots, we should really be worried about the creators of the technology. We want the creators of this technology to represent our values and represent our shared humanity.

Anna Patterson has led a distinguished career developing and deploying AI products, both at large technology companies and at startups.

A long-time executive at Google, which she first joined in 2004, Patterson led artificial intelligence efforts for years as the companys VP of Engineering. In 2017 she launched Googles AI venture capital fund Gradient Ventures, where today she invests in early-stage AI startups.

Patterson serves on the board of a number of promising AI startups including Algorithmia, Labelbox and test.ai. She is also a board director at publicly-traded Square.

Patterson on one question she asks herself before investing in any AI startup: Do I find myself constantly thinking about their vision and mission?

Daniela Rus is one of the worlds leading roboticists.

She is an MIT professor and the first female head of MITs Computer Science and Artificial Intelligence Lab (CSAIL), one of the largest and most prestigious AI research labs in the world. This makes her part of a storied lineage: previous directors of CSAIL (and its predecessor labs) over the decades have included AI legends Marvin Minsky, J.C.R. Licklider and Rodney Brooks.

Rus groundbreaking research has advanced the state of the art in networked collaborative robots (robots that can work together and communicate with one another), self-reconfigurable robots (robots that can autonomously change their structure to adapt to their environment), and soft robots (robots without rigid bodies).

Rus on a common misconception about AI: It is important for people to understand that AI is nothing more than a tool. Like any other tool, it is neither intrinsically good nor bad. It is solely what we choose to do with it. I believe that we can do extraordinarily positive things with AIbut it is not a given that that will happen.

Shivon Zilis has spent time on the leadership teams of several companies at AIs bleeding edge: OpenAI, Neuralink, Tesla, Bloomberg Beta.

She is the youngest board member at OpenAI, the influential research lab behind breakthroughs like GPT-3. At NeuralinkElon Musks mind-bending effort to meld the human brain with digital machinesZilis works on high-priority strategic initiatives in the office of the CEO.

Zilis on her attitude toward new technology development: Im astounded by how often the concept of building moats comes up. If you think the technology youre building is good for the world, why not laser focus on expanding your tech tree as quickly as possible versus slowing down and dividing resources to impede the progress of others?

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ECMarker: interpretable machine learning model identifies gene expression biomarkers predicting clinical outcomes and reveals molecular mechanisms of…

This article was originally published here

Bioinformatics. 2020 Nov 6:btaa935. doi: 10.1093/bioinformatics/btaa935. Online ahead of print.

ABSTRACT

MOTIVATION: Gene expression and regulation, a key molecular mechanism driving human disease development, remains elusive, especially at early stages. Integrating the increasing amount of population-level genomic data and understanding gene regulatory mechanisms in disease development are still challenging. Machine learning has emerged to solve this, but many machine learning methods were typically limited to building an accurate prediction model as a black box, barely providing biological and clinical interpretability from the box.

RESULTS: To address these challenges, we developed an interpretable and scalable machine learning model, ECMarker, to predict gene expression biomarkers for disease phenotypes and simultaneously reveal underlying regulatory mechanisms. Particularly, ECMarker is built on the integration of semi- and discriminative-restricted Boltzmann machines, a neural network model for classification allowing lateral connections at the input gene layer. This interpretable model is scalable without needing any prior feature selection and enables directly modeling and prioritizing genes and revealing potential gene networks (from lateral connections) for the phenotypes. With application to the gene expression data of non-small-cell lung cancer patients, we found that ECMarker not only achieved a relatively high accuracy for predicting cancer stages but also identified the biomarker genes and gene networks implying the regulatory mechanisms in the lung cancer development. In addition, ECMarker demonstrates clinical interpretability as its prioritized biomarker genes can predict survival rates of early lung cancer patients (P-value < 0.005). Finally, we identified a number of drugs currently in clinical use for late stages or other cancers with effects on these early lung cancer biomarkers, suggesting potential novel candidates on early cancer medicine.

AVAILABILITYAND IMPLEMENTATION: ECMarker is open source as a general-purpose tool at https://github.com/daifengwanglab/ECMarker.

CONTACT: [emailprotected]

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:33305308 | DOI:10.1093/bioinformatics/btaa935

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