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Category Archives: Machine Learning

Research Analyst / Associate / Fellow in Machine Learning and Artificial Intelligence job with NATIONAL UNIVERSITY OF SINGAPORE | 289568 – Times…

The Role

The Sustainable and Green Finance Institute (SGFIN) is a new university-level research institute in the National University of Singapore (NUS), jointly supported by the Monetary Authority of Singapore (MAS) and NUS. SGFIN aspires to develop deep research capabilities in sustainable and green finance, provide thought leadership in the sustainability space, and shape sustainability outcomes across the financial sector and the economy at large.

This role is ideally suited for those wishing to work in academic or industry research in quantitative analysis, particularly in the area of machine learning and artificial intelligence. The responsibilities of the role will include designing and developing various analytical frameworks to analyze structure, unstructured and non-traditional data related to corporate financial, environmental, and social indicators.

There are no teaching obligations for this position, and the candidate will have the opportunity to develop their research portfolio.

Duties and Responsibilities

The successful candidate will be expected to assume the following responsibilities:

Qualifications

Covid-19 Message

At NUS, the health and safety of our staff and students are one of our utmost priorities, and COVID-vaccination supports our commitment to ensure the safety of our community and to make NUS as safe and welcoming as possible. Many of our roles require a significant amount of physical interactions with students/staff/public members. Even for job roles that may be performed remotely, there will be instances where on-campus presences are required.

In accordance with Singapore's legal requirements, unvaccinated workers will not be able to work on the NUS premises with effect from 15 January 2022. As such, job applicants will need to be fully COVID-19 vaccinated to secure successful employment with NUS.

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Research Analyst / Associate / Fellow in Machine Learning and Artificial Intelligence job with NATIONAL UNIVERSITY OF SINGAPORE | 289568 - Times...

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Deploying machine learning to improve mental health | MIT News | Massachusetts Institute of Technology – MIT News

A machine-learning expert and a psychology researcher/clinician may seem an unlikely duo. But MITs Rosalind Picard and Massachusetts General Hospitals Paola Pedrelli are united by the belief that artificial intelligence may be able to help make mental health care more accessible to patients.

In her 15 years as a clinician and researcher in psychology, Pedrelli says it's been very, very clear that there are a number of barriers for patients with mental health disorders to accessing and receiving adequate care. Those barriers may include figuring out when and where to seek help, finding a nearby provider who is taking patients, and obtaining financial resources and transportation to attend appointments.

Pedrelli is an assistant professor in psychology at the Harvard Medical School and the associate director of the Depression Clinical and Research Program at Massachusetts General Hospital (MGH). For more than five years, she has been collaborating with Picard, an MIT professor of media arts and sciences and a principal investigator at MITs Abdul Latif Jameel Clinic for Machine Learning in Health (Jameel Clinic) on a project to develop machine-learning algorithms to help diagnose and monitor symptom changes among patients with major depressive disorder.

Machine learning is a type of AI technology where, when the machine is given lots of data and examples of good behavior (i.e., what output to produce when it sees a particular input), it can get quite good at autonomously performing a task. It can also help identify patterns that are meaningful, which humans may not have been able to find as quickly without the machine's help. Using wearable devices and smartphones of study participants, Picard and Pedrelli can gather detailed data on participants skin conductance and temperature, heart rate, activity levels, socialization, personal assessment of depression, sleep patterns, and more. Their goal is to develop machine learning algorithms that can intake this tremendous amount of data, and make it meaningful identifying when an individual may be struggling and what might be helpful to them. They hope that their algorithms will eventually equip physicians and patients with useful information about individual disease trajectory and effective treatment.

We're trying to build sophisticated models that have the ability to not only learn what's common across people, but to learn categories of what's changing in an individuals life, Picard says. We want to provide those individuals who want it with the opportunity to have access to information that is evidence-based and personalized, and makes a difference for their health.

Machine learning and mental health

Picard joined the MIT Media Lab in 1991. Three years later, she published a book, Affective Computing, which spurred the development of a field with that name. Affective computing is now a robust area of research concerned with developing technologies that can measure, sense, and model data related to peoples emotions.

While early research focused on determining if machine learning could use data to identify a participants current emotion, Picard and Pedrellis current work at MITs Jameel Clinic goes several steps further. They want to know if machine learning can estimate disorder trajectory, identify changes in an individuals behavior, and provide data that informs personalized medical care.

Picard and Szymon Fedor, a research scientist in Picards affective computing lab, began collaborating with Pedrelli in 2016. After running a small pilot study, they are now in the fourth year of their National Institutes of Health-funded, five-year study.

To conduct the study, the researchers recruited MGH participants with major depression disorder who have recently changed their treatment. So far, 48 participants have enrolled in the study. For 22 hours per day, every day for 12 weeks, participants wear Empatica E4 wristbands. These wearable wristbands, designed by one of the companies Picard founded, can pick up information on biometric data, like electrodermal (skin) activity. Participants also download apps on their phone which collect data on texts and phone calls, location, and app usage, and also prompt them to complete a biweekly depression survey.

Every week, patients check in with a clinician who evaluates their depressive symptoms.

We put all of that data we collected from the wearable and smartphone into our machine-learning algorithm, and we try to see how well the machine learning predicts the labels given by the doctors, Picard says. Right now, we are quite good at predicting those labels.

Empowering users

While developing effective machine-learning algorithms is one challenge researchers face, designing a tool that will empower and uplift its users is another. Picard says, The question were really focusing on now is, once you have the machine-learning algorithms, how is that going to help people?

Picard and her team are thinking critically about how the machine-learning algorithms may present their findings to users: through a new device, a smartphone app, or even a method of notifying a predetermined doctor or family member of how best to support the user.

For example, imagine a technology that records that a person has recently been sleeping less, staying inside their home more, and has a faster-than-usual heart rate. These changes may be so subtle that the individual and their loved ones have not yet noticed them. Machine-learning algorithms may be able to make sense of these data, mapping them onto the individuals past experiences and the experiences of other users. The technology may then be able to encourage the individual to engage in certain behaviors that have improved their well-being in the past, or to reach out to their physician.

If implemented incorrectly, its possible that this type of technology could have adverse effects. If an app alerts someone that theyre headed toward a deep depression, that could be discouraging information that leads to further negative emotions.Pedrelli and Picard are involving real users in the design process to create a tool thats helpful, not harmful.

What could be effective is a tool that could tell an individual The reason youre feeling down might be the data related to your sleep has changed, and the data relate to your social activity, and you haven't had any time with your friends, your physical activity has been cut down. The recommendation is that you find a way to increase those things, Picard says. The team is also prioritizing data privacy and informed consent.

Artificial intelligence and machine-learning algorithms can make connections and identify patterns in large datasets that humans arent as good at noticing, Picard says. I think there's a real compelling case to be made for technology helping people be smarter about people.

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Deploying machine learning to improve mental health | MIT News | Massachusetts Institute of Technology - MIT News

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How Artificial Intelligence and Machine Learning are Transforming the Life Sciences – Contract Pharma

Today, the life sciences industry is at a critical inflection point. Its public profile has elevated due to its success at quickly developing vaccines to combat the COVID-19 pandemic. It has also built up a lot of trust. Despite the persistent issue of vaccine hesitancy, health including life sciences rose up in the rankings to become the second most trusted sector after technology, according to the 2021 Edelman Trust Barometer.[1]While the life sciences industry rightly has the approval and trust of its stakeholders including heath companies, insurers, clinicians and patients such approbation gives rise to an important challenge going forward. This challenge is meeting those stakeholders ever-rising expectations.The rapid development and mass deployment of COVID-19 vaccines, including the pioneering mRNA vaccines, highlighted to stakeholders what the industry is capable of achieving. At the same time, new technological advances are opening up the possibility of the life sciences industry making other breakthroughs that will transform the health experiences of patients, while potentially saving millions of lives.Artificial intelligence- and machine learning-enabled transformationWith the maturation and advancement of artificial intelligence (AI), it is set to have a measurable impact on the life sciences industry. AI is enabled by complex algorithms that are designed to make decisions and solve problems. In combination with machine learning (ML) and natural language processing, which make it possible for the algorithms to learn from experiences, AI and ML will help life sciences companies develop treatments faster and more efficiently in the future, reducing the costs of health care, while making it more accessible to patients.We already know that AI and ML have the potential to transform the following processes in life sciences:Drug development.Thanks to its ability to process and interpret large data sets, AI and ML can be deployed to design the right structure for drugs and make predictions around bioactivity , toxicity and physicochemical properties. Not only will this input speed up the drug development process, but it will help to ensure that the drugs deliver the optimal therapeutic response when they are administered to patients.Diagnostics.AI and ML are effective at identifying characteristics in images that cannot be perceived by the human brain. As a result, it can play a vital role in diagnosing cancer. Research by the National Cancer Institute in the US suggests that AI can be used to improve screening for cervical and prostate cancer and identify specific gene mutations from tumor pathology images. There are already several commercial applications in the market. Going forward, AI may also be used to diagnose other conditions, including heart disease and diabetic retinopathy. By enabling early detection of life-threatening diseases, AI will help people enjoy longer, healthier lives. Clinical trials .The fashion in which clinical trials have been designed and conducted have not materially changed over the last decades, until the pandemic brought about necessary change to help transform some components of the clinical trial process, such as study monitoring and patient enrollment. As the research and development cost comprises 17% of total pharma revenue and has increased from 14% over the last 10 years,[2] there are calls for long overdue decentralization to be brought about by technology. Some commercially available platforms have made this concept a reality.Supply chain. By analyzing longitudinal data, AI and ML can identify systemic issues in the pharmaceutical manufacturing process, highlight production bottlenecks, predict completion times for corrective actions, reduce the length of the batch disposition cycle and investigate customer complaints. It can also monitor in-line manufacturing processes to ensure the safety and quality of drugs. These interventions will give life sciences companies confidence that their manufacturing processes are operating at a high standard and not putting the organization in breach of regulations. Importantly, the bottlenecks caused by the pandemic tested the resiliency of the entire supply chain ecosystem. Furthermore, life sciences companies can improve their efficiency by applying AI to their supply chain management and logistics processes, aligning production with demand and with an AI-enabled sales and operations planning process.Commercial and regulatory processes.Reviewing promotional content for compliance purposes has been a necessary, yet constricting, stage gate for any biopharma company. The current medical, legal and regulatory review processes for approving product marketing materials are painfully slow and can be inconsistent, leading to repetitive cycle times. Promotional content is the single most important source of information of newly approved products, given the paucity of peer review literature at launch. This holds back approved medications from reaching providers and patients sooner. Now, AI and ML have been proven to be utilized to significantly reduce the medical, legal and regulatory review time, while improving the accuracy of the content. This will improve the speed and reliability of the processes, enabling therapies to get to market quicker.Beginning of a new digital era with broader utilization of AI and MLWe are only in the early stages of deploying AI and ML in life sciences. And while we can already see their promise, the industry is likely to find numerous future use cases for the technology that we cannot even begin to conceive of today. There already are early signs as to how AI can be incorporated into surgical robots, with the theory that AI-powered surgical robots may one day be allowed to operate independently of human control. Whether that ever happens is likely to depend on regulatory frameworks and legal liabilities, rather than technological advances.Inevitably, there will be a massive amount of change as we move past the current inflection point. The proliferating variants of the severe acute respiratory syndrome coronavirus, such as Omicron, and the successful deployment of mRNA technology leading to rapid development of the COVID-19 vaccines are putting pressure on the life sciences industry to do more and faster when it comes to developing and manufacturing treatments for cancers and other diseases. So how can it rise to this challenge? To meet the expectations of its stakeholders, the life sciences industry will undoubtedly need to exploit the full potential of AI and ML.[1] Kristy Graham, Science and Public Health: Transparency is the Road to Trust, Daniel J. Edelman Holdings website, https://www.edelman.com/trust/2021-trust-barometer/insights/science-public-health#top, accessed December 2021.[2] Capital IQ report about top 25 biopharma companies, 2021.Arda Ural, PhD, is the EY Americas Industry Markets leader for EYs Health Sciences and Wellness Practice.Arda has nearly 30 years experience in pharma, biotech and medtech, including general management, new product development, corporate strategy and M&A. Prior to joining EY, he was a Managing Director at a strategy consulting firm and worked as a VP of Strategic Marketing and a BU lead at a medtech company. Arda holds a PhD in General Management and Finance and an MBA from Marmara University in Istanbul, as well as an MSc and BSc in Mechanical Engineering from Boazii University.The views expressed by the author are not necessarily those of Ernst & Young LLP or other members of the global EY organization.

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Debit: The Long Count review Mayans, machine learning and music – The Guardian

There is an uncanniness in listening to a musical instrument you have never heard being played for the first time. As your brain makes sense of a new sound, it tries to frame it within the realm of familiarity, producing a tussle between the known and unknown.

The second album from Mexican-American producer Delia Beatriz, AKA Debit, embraces this dissonance. Taking the flutes of the ancient Mayan courts as her raw material and inspiration, Beatriz used archival recordings from the Mayan Studies Institute at the Universidad Nacional Autnoma de Mxico to create a digital library of their sounds. She then processed these ancient samples through a machine-learning program to create woozy, ambient soundscapes.

Since no written music has survived from the Mayan civilisation, Beatriz crafts a new language for these ancient wind instruments, straddling the electronic world of her 2017 debut Animus and the dilatory experimentalism of ambient music. The resulting 10 tracks make for a deliciously strange listening experience.

Opener 1st Day establishes the undulating tones that unify the record. They flutter like contemplative humming and veer from acoustic warmth to metallic note-bending. Each track is given a numbered day and time, as if documenting the passage of a ritual, and echoes resonate down the record: whistles appear like sirens during the moans of 1st Night and 3rd Night; snatches of birdsong are tucked between the reverb of 2nd Day and 5th Day.

The Long Count of the records title seems to express the linear passage of time itself, one replicated in the eternal, fluid flute tones. We hear in them the warmth of the human breath that first produced their sound, as well as Beatrizs electronic filtering that extends their notes until they imperceptibly bleed into one another and fuzz like keys on a synth. It is a startlingly original and enveloping sound that leaves us with that ineffable feeling: the past unearthed and made new once more.

Korean composer Park Jiha releases her third album, The Gleam (tak:til), a solo work featuring uniquely sparse compositions of saenghwang mouth organ, piri oboe and yanggeum dulcimer. British-Ghanaian rapper KOG brings his debut LP, Zone 6, Agege (Heavenly Sweetness), a deeply propulsive mix of English, Pidgin and Ga lyrics set to Afrobeat fanfares. Cellist and composer Ana Carla Maza releases her latest album, Baha (Persona Editorial), an affecting combination of Cuban son, bossa and chanson in homage to the music of her birthplace of Havana.

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Research Engineer, Machine Learning job with NATIONAL UNIVERSITY OF SINGAPORE | 279415 – Times Higher Education (THE)

Job Description

Vessel Collision Avoidance System is a real-time framework to predict and prevent vessel collisions based on historical movement of vessels in heavy traffic regions such as Singapore strait. We are looking for talented developers to join our development team to help us develop machine learning and agent-based simulation models to quantify vessel collision risk at Singapore strait and port. If you are data curious, excited about deriving insights from data, and motivated by solving a real-world problem, we want to hear from you.

Qualifications

A B.Sc. in a quantitative field (e.g., Computer Science, Statistics, Engineering, Science) Good coding habit in Python and able to solve problems in a fast pace Familiar with popular machine learning models Eager to learn new things and has passion in work Take responsibility, team oriented, and result oriented The ability to communicate results clearly and a focus on driving impact

More Information

Location: Kent Ridge CampusOrganization: EngineeringDepartment : Industrial Systems Engineering And ManagementEmployee Referral Eligible: NoJob requisition ID : 7334

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Research Engineer, Machine Learning job with NATIONAL UNIVERSITY OF SINGAPORE | 279415 - Times Higher Education (THE)

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Artificial Intelligence and Machine Learning drive FIAs initiatives for financial inclusivity in India – Express Computer

In an exclusive video interview with Express Computer, Seema Prem, Co-founder and CEO, FIA Global shares about the companys investment in Artificial Intelligence and Machine Learning in the last five years for financial inclusivity in the country.

FIA, a financial inclusivity neo bank delivers financial services through its app, Finvesta. The app employs AI, facial recognition and Natural Language Processing to aggregate, redesign, recommend and deliver financial products at scale. The app uses icons for user interface, for ease of use where literacy levels are low.

Seema Prem, Co-founder and CEO, FIA says, We have reaped significant benefits by incorporating AI and ML in our operations. So we handle very tiny transactions and big data. The algorithm modules, especially rule-based modules have reached a certain performance plateau. AI and ML have been incorporated for smart bot applications for servicing the customers, audit where we look at embedding facial recognition, pattern detection for predicting the performance of business, analysing large volumes of data and many more. It helps us to ensure that manual intervention comes down significantly. Last year, after the pandemic we automated like there is no tomorrow and that automation has resulted in huge productivity for us.

FIAs role in the financial inclusivity in India is largely associated with Pradham Mantri Jan Dhan Yojana where they tie-up with banks to set up centres in very remote and secluded regions of India like Uri, Kargil, Kedarnath, Kanyakumari, etc.

Prem states, We work in 715 districts of the country in areas like a bank branch that have never been there. Once the bank account opens in such areas then people get the confidence in remote areas for banking. Eventually, we try to fulfil the needs of people for other products like pension, insurance, healthcare, livestock loans, vehicle insurance and property insurance. We provide doorstep delivery of pension to our customers. So our services also endure community engagement besides financial inclusivity targeting various special groups like women and old age people.

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Artificial Intelligence and Machine Learning drive FIAs initiatives for financial inclusivity in India - Express Computer

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